introduction for lung cancer research paper

Lung Cancer Research Results and Study Updates

See Advances in Lung Cancer Research for an overview of recent findings and progress, plus ongoing projects supported by NCI.

Lorlatinib (Lorbrena) is superior to crizotinib (Xalkori) as an initial treatment for people with ALK-positive advanced non-small cell lung cancer, according to new clinical trial results. Treatment with lorlatinib also helped prevent new brain metastases.

The immunotherapy drug durvalumab (Imfinzi) can help people with early-stage small cell lung cancer live longer, results from a large clinical trial show. Three years after starting treatment, nearly 60% of people who received the drug were still alive.

FDA has approved alectinib (Alecensa) as adjuvant therapy for people with lung cancer who have ALK-positive tumors. In a clinical trial, alectinib helped people live longer after surgery without their cancer returning than chemotherapy.

The results of the clinical trial that led to FDA’s 2023 approval of repotrectinib (Augtyro) for lung cancers with ROS1 fusions have been published. The drug shrank tumors in 80% of people receiving the drug as an initial treatment.

A collection of material about the ALCHEMIST lung cancer trials that will examine tumor tissue from patients with certain types of early-stage, completely resected non-small cell lung cancer for gene mutations in the EGFR and ALK genes, and assign patients with these gene mutations to treatment trials testing post-surgical use of drugs targeted against these mutations.

Tarlatamab, a new type of targeted immunotherapy, shrank small cell lung cancer (SCLC) tumors in more than 30% of participants in an early-stage clinical trial. Participants had SCLC that had progressed after previous treatments with other drugs.

For people with lung cancer and medullary thyroid cancer whose tumors have changes in the RET gene, selpercatinib improved progression-free survival compared with other common treatments, according to new clinical trial results.

In the ADAURA clinical trial, people with early-stage lung cancer treated with osimertinib (Tagrisso) after surgery lived longer than people treated with a placebo after surgery. Despite some criticisms about its design, the trial is expected to change patient care.

For certain people with early-stage non-small cell lung cancer, sublobar surgery to remove only a piece of the affected lung lobe is as effective as surgery to remove the whole lobe, new research shows.

Pragmatica-Lung is a clinical trial for people with non-small cell lung cancer that has spread beyond the lungs (stage 4 cancer). The trial will help confirm if the combination of pembrolizumab and ramucirumab helps people with advanced lung cancer live longer.

On August 11, the Food and Drug Administration (FDA) gave accelerated approval to trastuzumab deruxtecan (Enhertu) for adults with non-small cell lung cancer (NSCLC) that has a specific mutation in the HER2 gene. Around 3% of people with NSCLC have this kind of HER2 mutation.

Giving people with early-stage lung cancer the immunotherapy drug nivolumab (Opdivo) and chemotherapy before surgery can substantially delay the progression or return of their cancer, a large clinical trial found.

Atezolizumab (Tecentriq) is now the first immunotherapy approved by FDA for use as an additional, or adjuvant, treatment for some patients with non-small cell lung cancer. The approval was based on results of a clinical trial called IMpower010.

Quitting smoking after a diagnosis of early-stage lung cancer may help people live longer, a new study finds. The study, which included more than 500 patients, also found that quitting smoking delayed the cancer from returning or getting worse.

NCI scientists and their international collaborators have found that the majority of lung cancers in never smokers arise when mutations caused by natural processes in the body accumulate. They also identified three subtypes of lung cancer these individuals.

FDA has approved the first KRAS-blocking drug, sotorasib (Lumakras). The approval, which covers the use of sotorasib to treat some patients with advanced lung cancer, sets the stage for other KRAS inhibitors already in development, researchers said.

Combining the chemotherapy drug topotecan and the investigational drug berzosertib shrank tumors in some patients with small cell lung cancer, results from an NCI-supported phase 1 clinical trial show. Two phase 2 trials of the combination are planned.

Mortality rates from the most common lung cancer, non-small cell lung cancer (NSCLC), have fallen sharply in the United States in recent years, due primarily to recent advances in treatment, an NCI study shows.

In a study of more than 50,000 veterans with lung cancer, those with mental illness who received mental health treatment—including for substance use—lived substantially longer than those who didn’t participate in such programs.

FDA has granted accelerated approval for selpercatinib (Retevmo) to treat certain patients with thyroid cancer or non-small cell lung cancer whose tumors have RET gene alterations. The drug, which works by blocking the activity of RET proteins, was approved based on the results of the LIBRETTO-001 trial.

Osimertinib (Tagrisso) improves survival in people with non-small cell lung cancer with EGFR mutations, updated clinical trial results show. People treated with osimertinib lived longer than those treated with earlier-generation EGFR-targeted drugs.

A large clinical trial showed that adding the immunotherapy drug durvalumab (Imfinzi) to standard chemotherapy can prolong survival in some people with previously untreated advanced small cell lung cancer.

The investigational drug selpercatinib may benefit patients with lung cancer whose tumors have alterations in the RET gene, including fusions with other genes, according to results from a small clinical trial.

FDA has approved entrectinib (Rozlytrek) for the treatment of children and adults with tumors bearing an NTRK gene fusion. The approval also covers adults with non-small cell lung cancer harboring a ROS1 gene fusion.

Clinical recommendations on who should be screened for lung cancer may need to be reviewed when it comes to African Americans who smoke, findings from a new study suggest.

Use of a multipronged approach within hospitals, including community centers, not only eliminated treatment disparities among black and white patients with early-stage lung cancer, it also improved treatment rates for all patients, results from a new study show.

In everyday medical care, there may be more complications from invasive diagnostic procedures performed after lung cancer screening than has been reported in large studies.

The Lung Cancer Master Protocol, or Lung-MAP, is a precision medicine research study for people with advanced non-small cell lung cancer that has continued to grow after treatment. Patients are assigned to different study drug combinations based on the results of genomic profiling of their tumors.

On December 6, 2018, the Food and Drug Administration (FDA) approved atezolizumab (Tecentriq) in combination with a standard three-drug regimen as an initial treatment for advanced lung cancer that does not have EGFR or ALK mutations.

A new study has identified a potential biomarker of early-stage non–small cell lung cancer (NSCLC). The biomarker, the study’s leaders said, could help diagnose precancerous lung growths and early-stage lung cancers noninvasively and distinguish them from noncancerous growths.

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Lung cancer

Affiliations.

1 Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, VIC, Australia.
2 Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
3 Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. Electronic address: [email protected].
PMID: 34273294
DOI: 10.1016/S0140-6736(21)00312-3

Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer-related deaths worldwide with an estimated 2 million new cases and 1·76 million deaths per year. Substantial improvements in our understanding of disease biology, application of predictive biomarkers, and refinements in treatment have led to remarkable progress in the past two decades and transformed outcomes for many patients. This seminar provides an overview of advances in the screening, diagnosis, and treatment of non-small-cell lung cancer and small-cell lung cancer, with a particular focus on targeted therapies and immune checkpoint inhibitors.

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Conflict of interest statement

Declaration of interests BJS reports personal fees from Pfizer, Novartis, Roche/Genentech, AstraZeneca, Merck, Bristol Myers Squibb, Amgen, and Loxo Oncology outside the submitted work. JFG has served as a consultant or received honoraria from Bristol-Myers Squibb, Genentech, Ariad/Takeda, Loxo/Lilly, Blueprint, Oncorus, Regeneron, Gilead, Helsinn, EMD Serono, AstraZeneca, Pfizer, Incyte, Novartis, Merck, Agios, Amgen, and Array; has had research support from Novartis, Genentech/Roche, Ariad/Takeda, Bristol-Myers Squibb, Tesaro, Moderna, Blueprint, Jounce, Array Biopharma, Merck, Adaptimmune, and Alexo; and has an immediate family member who is an employee of Ironwood Pharmaceuticals. LVS reports grants and personal fees from AstraZeneca; grants from Novartis and Boehringer Ingelheim; grants and consulting fees from Genentech Blueprint and Merrimack Pharmaceuticals; and consulting fees from Janssen and grants from LOXO, all outside the submitted work. LVS has a patent about treatment of EGFR-mutant cancer pending. RSH reports honoraria from Novartis, Merck KGaA, Daichii Sankyo, Pfizer, Roche, Apollomics, Tarveda, and Boehringer Ingelheim; and grants from Novartis, Genentech Roche, Corvus, Incyte, Exelixis, Abbvie, Daichii Sankyo, Agios, Mirati, Turning Point, and Lilly when writing this Seminar. AAT declares no competing interests.

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Advancement in Lung Cancer Diagnosis: A Comprehensive Review of Deep Learning Approaches

First Online: 08 August 2024

Cite this chapter

Djamel Bouchaffra 1 , 2 ,
Faycal Ykhlef 1 &
Samir Benbelkacem 1

Part of the book series: Interdisciplinary Cancer Research

Lung cancer continues to pose a significant global health challenge. To overcome this challenge, continuous advancements are being achieved in diagnostic methodologies to enhance early detection and improve patient outcomes. This chapter provides a thorough examination of recent progress in lung cancer diagnosis through an extensive survey of deep learning approaches. Focusing on the integration of artificial intelligence (AI) techniques with medical imaging, the chapter encompasses an analysis of convolutional neural networks (CNNs), recurrent neural networks (RNNs), including long short-term memory (LSTMs) networks, and generative-pretrained transformers (GPTs) or large language models (LLMs). The chapter delves into the evolution of deep learning models for lung cancer detection, emphasizing their performance in image classification, lesion segmentation, and overall diagnostic accuracy. Additionally, we also showcase the literature that explores the integration of diverse imaging modalities, such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), within deep learning frameworks to enhance the robustness and reliability of diagnostic systems. Furthermore, the review addresses the challenges inherent in the exploration of deep learning in lung cancer diagnosis, including issues related to data quality, model interpretability, and generalizability. Strategies to address these challenges, such as transfer learning, data augmentation (based on generative adversarial networks), and transformers, are thoroughly discussed. The comprehensive analysis presented in this chapter aims to provide a consolidated understanding of the current landscape of deep learning approaches in lung cancer diagnosis. By highlighting recent advancements, challenges, and potential solutions, this chapter contributes to the ongoing dialogue within the scientific community, fostering the development of more effective and reliable tools for early detection and management of lung cancer.

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Djamel Bouchaffra, Faycal Ykhlef & Samir Benbelkacem

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Bouchaffra, D., Ykhlef, F., Benbelkacem, S. (2024). Advancement in Lung Cancer Diagnosis: A Comprehensive Review of Deep Learning Approaches. In: Interdisciplinary Cancer Research. Springer, Cham. https://doi.org/10.1007/16833_2024_302

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Lung Neoplasms
Neoplasms by Site
Thoracic Neoplasms
Respiratory Tract Neoplasms
Lung Cancer

Lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for the highest mortality rates among both men and women.
Smoking is the leading cause of lung cancer, responsible for approximately 85% of all cases.
Lung cancer is often diagnosed at advanced stages when treatment options are limited.
Screening high risk individuals has the potential to allow early detection and to dramatically improve survival rates.
Primary prevention (such as tobacco control measures and reducing exposure to environmental risk factors) can reduce the incidence of lung cancer and save lives.

Lung cancer is a type of cancer that starts when abnormal cells grow in an uncontrolled way in the lungs. It is a serious health issue that can cause severe harm and death.

Symptoms of lung cancer include a cough that does not go away, chest pain and shortness of breath.

It is important to seek medical care early to avoid serious health effects. Treatments depend on the person’s medical history and the stage of the disease.

The most common types of lung cancer are non-small cell carcinoma (NSCLC) and small cell carcinoma (SCLC). NSCLC is more common and grows slowly, while SCLC is less common but often grows quickly.

Lung cancer is a significant public health concern, causing a considerable number of deaths globally. GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer (IARC) show as lung cancer remains the leading cause of cancer death, with an estimated 1.8 million deaths (18%) in 2020.

Smoking tobacco (including cigarettes, cigars, and pipes) is the primary risk factor for lung cancer but it can also affect non-smokers. Other risk factors include exposure to secondhand smoke, occupational hazards (such as asbestos, radon and certain chemicals), air pollution, hereditary cancer syndromes, and previous chronic lung diseases.

Lung cancer can cause several symptoms that may indicate a problem in the lungs.

The most common symptoms include:

cough that does not go away
shortness of breath
coughing up blood (haemoptysis)
weight loss with no known cause
lung infections that keep coming back.

Early symptoms may be mild or dismissed as common respiratory issues, leading to delayed diagnosis.

Not smoking tobacco is the best way to prevent lung cancer.

Other risk factors to avoid include:

secondhand smoke
air pollution
workplace hazards like chemicals and asbestos.

Early treatment can prevent lung cancer from becoming worse and spreading to other parts of the body.

Prevention of lung cancer include primary and secondary prevention measures. Primary prevention aims to prevent the initial occurrence of a disease through risk reduction and promoting healthy behaviour. In public health, these preventive measures include smoking cessation, promoting smoke-free environments, implementing tobacco control policies, addressing occupational hazards, and reducing air pollution levels.

Secondary prevention for lung cancer involves screening methods that aim to detect the disease in its early stages, before symptoms become apparent and can be indicated for high-risk individuals. In this population, early detection can significantly increase the chances of successful treatment and improve outcomes. The primary screening method for lung cancer is low-dose computed tomography (LDCT).

Diagnostic methods for lung cancer include physical examination, imaging (such as chest X-rays, computed tomography scans, and magnetic resonance imaging), examination of the inside of the lung using a bronchoscopy, taking a sample of tissue (biopsy) for histopathology examination and definition of the specific subtype (NSCLC versus SCLC), and molecular testing to identify specific genetic mutations or biomarkers to guide the best treatment option.

Treatment and care

Treatments for lung cancer are based on the type of cancer, how much it has spread, and the person’s medical history. Early detection of lung cancer can lead to better treatments and outcomes.

Treatments include:

radiotherapy (radiation)
chemotherapy
targeted therapy
immunotherapy.

Surgery is often used in the early stages of lung cancer if the tumour has not spread to other areas of the body. Chemotherapy and radiation therapy can help shrink the tumour.

Doctors from several disciplines often work together to provide treatment and care of people with lung cancer.

Supportive care is important for people with lung cancer. It aims to manage symptoms, provide pain relief, and give emotional support. It can help to increase quality of life for people with lung cancer and their families.

Stages of care

a) Early stage disease : The primary treatment for early stage lung cancer (i.e. tumour limited to the lung, with no metastatic dissemination to distant organs or lymph nodes) is surgical removal of the tumour through procedures such as lobectomy, segmentectomy, or wedge resection. Neoadjuvant therapy (chemotherapy and/or radiation therapy before surgery) can help reduce tumour size, making it more manageable for surgical removal. Adjuvant treatment (chemotherapy and/or radiation therapy) is very often recommended after surgery to reduce the risk of cancer recurrence. In cases where surgery is not feasible, radiation therapy or stereotactic body radiation therapy (SBRT) may be used as the primary treatment. Targeted therapy and immunotherapy may also be considered based on specific tumour characteristics. Individualized treatment plans should be discussed with healthcare professionals.

b) Advanced disease: The treatment for metastatic stage lung cancer, where the cancer has spread to distant organs or lymph nodes, is based on various factors, including the patient's overall health, the extent and location of metastases, histology, genetic profile, and individual preferences. The primary goal is to prolong survival, alleviate symptoms, and improve quality of life. Systemic therapies, such as chemotherapy, targeted therapy, and immunotherapy, play a crucial role in the treatment of metastatic lung cancer.

Chemotherapy is often the first-line treatment for the majority of patients around the world and involves the use of drugs that circulate throughout the body to kill cancer cells. Combination chemotherapy regimens are commonly used, and the choice of drugs depends on factors such as the histological type of the cancer and the patient's general health conditions. Targeted therapy, designed to block the signalling pathways that drive the growth of cancer cells, is an important option for patients with specific genetic mutations or biomarkers identified in their tumour. Immunotherapy, specifically immune checkpoint inhibitors, has revolutionized the treatment of metastatic lung cancer. These drugs help to stimulate the immune system to recognize and attack cancer cells. Local treatments, such as radiation therapy and surgery, may be used to manage specific metastatic sites or alleviate symptoms caused by tumour growth.

Clinical Trials

Clinical trials offer opportunities to access novel treatments or experimental therapies for patients. Participation in clinical trials helps advance medical knowledge and potentially offers new treatment options.

WHO response

WHO recognizes the significant impact of lung cancer on global health and has implemented several initiatives to address the disease comprehensively. The WHO's response focuses on tobacco control, cancer prevention, early detection, and improving access to quality treatment and care. WHO supports countries in implementing evidence-based tobacco control policies, including increasing tobacco taxes, enforcing comprehensive bans on tobacco advertising, promotion, and sponsorship, and implementing strong graphic health warnings on tobacco products.

The Organization also promotes cancer prevention strategies by advocating for healthy lifestyles, including regular physical activity, a healthy diet, and minimizing exposure to environmental risk factors. Additionally, WHO supports early detection programs and encourages countries to implement screening measures for high-risk populations to detect lung cancer at earlier stages when treatment options are more effective. Last, WHO works towards ensuring access to quality treatment and care for lung cancer patients by providing technical guidance to member states, promoting equitable access to essential cancer medicines, and fostering international collaboration to share best practices and improve cancer care outcomes.

International Agency for Research on Cancer: Lung cancer

WHO's work on tobacco cessation

WHO's work on cancer

ESMO Clinical Practice Guidelines: Lung and Chest Tumours

The N 6 -methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma

Cancer Discov 2024;XX:1–21

H. He is a lead contact.

Current address for S. Chen: West China School of Public Health, West China Fourth Hospital, and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.

S. Wang and Y. Zeng contributed equally to this article.

Funder(s): Princess Margaret Cancer Foundation (PMCF)
Award Id(s): 886012001223
Principal Award Recipient(s): H.H. He
Funder(s): Canada Foundation for Innovation (CFI)
Award Id(s): CFI32372
Funder(s): Natural Sciences and Engineering Research Council of Canada (NSERC)
Award Id(s): 498706
Award Id(s): 06305
Principal Award Recipient(s): M.F. Moran
Funder(s): Canadian Cancer Society (CCS)
Award Id(s): 703800
Funder(s): Canadian Institutes of Health Research (CIHR)
Award Id(s): 142246 , 152863 , 152864 , 159567
Award Id(s): 364778
Award Id(s): 148395
Principal Award Recipient(s): M.S. Tsao
Funder(s): Terry Fox Research Institute (TFRI)
Award Id(s): 1090 , 1124

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Proof August 13 2024
Accepted Manuscript June 25 2024

Shiyan Wang , Yong Zeng , Lin Zhu , Min Zhang , Lei Zhou , Weixiong Yang , Weishan Luo , Lina Wang , Yanming Liu , Helen Zhu , Xin Xu , Peiran Su , Xinyue Zhang , Musaddeque Ahmed , Wei Chen , Moliang Chen , Sujun Chen , Mykhaylo Slobodyanyuk , Zhongpeng Xie , Jiansheng Guan , Wen Zhang , Aafaque Ahmad Khan , Shingo Sakashita , Ni Liu , Nhu-An Pham , Paul C. Boutros , Zunfu Ke , Michael F. Moran , Zongwei Cai , Chao Cheng , Jun Yu , Ming S. Tsao , Housheng H. He; The N 6 -methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma. Cancer Discov 2024; https://doi.org/10.1158/2159-8290.CD-23-1212

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Comprehensive N 6 -methyladenosine (m 6 A) epitranscriptomic profiling of primary tumors remains largely uncharted. Here, we profiled the m 6 A epitranscriptome of 10 nonneoplastic lung tissues and 51 lung adenocarcinoma (LUAD) tumors, integrating the corresponding transcriptomic, proteomic, and extensive clinical annotations. We identified distinct clusters and genes that were exclusively linked to disease progression through m 6 A modifications. In comparison with nonneoplastic lung tissues, we identified 430 transcripts to be hypo-methylated and 222 to be hyper-methylated in tumors. Among these genes, EML4 emerged as a novel metastatic driver, displaying significant hypermethylation in tumors. m 6 A modification promoted the translation of EML4 , leading to its widespread overexpression in primary tumors. Functionally, EML4 modulated cytoskeleton dynamics by interacting with ARPC1A, enhancing lamellipodia formation, cellular motility, local invasion, and metastasis. Clinically, high EML4 protein abundance correlated with features of metastasis. METTL3 small-molecule inhibitor markedly diminished both EML4 m 6 A and protein abundance and efficiently suppressed lung metastases in vivo .

Significance: Our study reveals a dynamic and functional epitranscriptomic landscape in LUAD, offering a valuable resource for further research in the field. We identified EML4 hypermethylation as a key driver of tumor metastasis, highlighting a novel therapeutic strategy of targeting EML4 to prevent LUAD metastasis.

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Supplementary data.

All methods used in this study.

Sample information and gene list of NL-specific, tumor-specific and common methylated genes.

Gene enrichment of G1-3 groups.

m6A, mRNA and protein level-based clustering.

BLVRA m6A and mRNA expression levels in the validation cohorts.

Summary and gene enrichment of hyper-, hypo-, NL-specific and tumor-specific methylated genes.

Sample information and analysis in the internal cohort.

RNA-seq analysis of EML4-overexpressing, EML4-knockdown and control NSCLC cell samples.

Information of EML4 and IgG control antibody precipitated proteins identified by Co-IP/MS.

Sequence information of primers, siRNAs, shRNAs and sgRNAs.

Characteristics of m6A peaks and methodological comparison for m6A level calculation.

mRNA abundance of m6A regulators in lung tissues and genomic characteristics of G1, G2 and G3 gene groups.

Differences between m6A level-derived patient subtypes.

Association of m6A with clinical features and patient survival.

The function of BLVRA m6A.

Global m6A hypo-methylation in LUAD.

The function of EML4 m6A.

The oncogenic function of EML4.

The effects of ARPC1A depletion in LUAD.

Analysis of EML4 and METTL3 protein levels in LUAD patient tumors and functional assays of METTL3 perturbation.

Toxicity assessment of STM2457 treatment.

Live cell imaging of control A549 cells in wound healing assay.

Live cell imaging of EML4-overexpressing A549 cells in wound healing assay.

Live cell imaging of control H358 cells in wound healing assay.

Live cell imaging of EML4-overexpressing H358 cells in wound healing assay.

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Cancers (Basel)

Deep Learning Techniques to Diagnose Lung Cancer

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Simple Summary

This study investigates the latest achievements, challenges, and future research directions of deep learning techniques for lung cancer and pulmonary nodule detection. Hopefully, these research findings will help scientists, investigators, and clinicians develop new and effective medical imaging tools to improve lung nodule diagnosis accuracy, sensitivity, and specificity.

Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

1. Introduction

Lung cancer is the most frequent cancer and the cause of cancer death, with the highest morbidity and mortality in the United States [ 1 ]. In 2018, GLOBOCAN estimated approximately 2.09 million new cases and 1.76 million lung cancer-related deaths [ 2 ]. Lung cancer cases and deaths have increased significantly globally [ 2 ]. Approximately 85–88% of lung cancer cases are non-small cell lung carcinoma (NSCLS), and about 12–15% of lung cancer cases are small cell lung cancer (SCLC) [ 3 ]. Early lung cancer diagnosis and intervention are crucial to increase the overall 5-year survival rate due to the invasiveness and heterogeneity of lung cancer [ 4 ].

Over the past two decades, various medical imaging techniques, such as chest X-ray, positron emission tomography (PET), magnetic resonance imaging (MRI), computed tomography (CT), low-dose CT (LDCT), and chest radiograph (CRG), have been extensively investigated for lung nodule detection. Although CT is the golden standard imaging tool for lung nodule detection, it can only detect apparent lung cancer with high false-positive rates and produces harmful X-ray radiation [ 5 ]. LDCT has been proposed to reduce harmful radiation to detect lung cancer [ 6 ]. However, cancer-related deaths were concentrated in subjects undergoing LDCT. 2-deoxy-18F-fluorodeoxyglucose (18F-FDG) PET has been developed to improve the detection performance of lung cancer [ 7 ]. 18F-FDG PET produces semi-quantitative parameters of tumor glucose metabolism, which is helpful in the diagnosis of NSCLC [ 8 ]. However, 18F-FDG PET requires further evaluation of patients with NSCLC. Some new imaging techniques, such as magnetic induction tomography (MIT), have been developed for early-stage cancer cell detection [ 9 ]. However, this technique lacks clinical validation of human subjects.

Many computer-aided detection (CAD) systems have been extensively studied for lung cancer detection and classification [ 10 , 11 ]. Compared to trained radiologists, CAD systems provide better lung nodules and cancer detection performance in medical images. Generally, the CAD-based lung cancer detection system includes four steps: image processing, extraction of the region of interest (ROI), feature selection, and classification. Among these steps, feature selection and classification play the most critical roles in improving the accuracy and sensitivity of the CAD system, which relies on image processing to capture reliable features. However, benign and malignant nodule classification is a challenge. Many investigators have applied deep learning techniques to help radiologists make more accurate diagnoses [ 12 , 13 , 14 , 15 ]. Previous studies have confirmed that deep learning-based CAD systems can effectively improve the efficiency and accuracy of medical diagnosis, especially for diagnosing various common cancers, such as lung and breast cancers [ 16 , 17 ]. Deep learning-based CAD systems can automatically extract high-level features from original images using different network structures than traditional CAD systems. However, deep learning-based CAD systems have some limitations, such as low sensitivity, high FP, and time consumption. Therefore, a rapid, cost-effective, and highly sensitive deep learning-based CAD system for lung cancer prediction is urgently needed.

The deep learning-based lung imaging techniques research mainly includes pulmonary nodule detection, segmentation, and classification of benign and malignant pulmonary nodules. Researchers mainly focus on developing new network structures and loss functions to improve the performance of deep learning models. Several research groups have recently published review papers on deep learning techniques [ 18 , 19 , 20 ]. However, deep learning techniques have developed rapidly, and many new methods and applications have emerged every year. This research has appeared with content that previous studies cannot cover.

This paper presents recent achievements in lung cancer segmentation, detection, and classification using deep learning methods. This study highlights current state-of-the-art deep learning-based lung cancer detection methods. This paper also highlights recent achievements, relevant research challenges, and future research directions. The rest of the paper is structured as follows. Section 2 describes the currently available medical lung imaging techniques for lung cancer detection; Section 3 reviews some recently developed deep learning-based imaging techniques; Section 4 presents lung cancer prediction using deep learning techniques; Section 5 describes the current challenges and future research directions of deep learning-based lung imaging methods; and Section 6 concludes this study.

2. Lung Imaging Techniques

Medical imaging tools help radiologists diagnose lung disease. Among these medical imaging approaches, CT offers more advantages, including size, location, characterization, and lesion growth, which could identify lung cancer and nodule information. 4D CT provides more precise targeting of the administered radiation, which significantly impacts lung cancer management [ 21 ]. Lakshmanaprabu et al. [ 22 ] developed an automatic detection system based on linear discriminate analysis (LDA) and an optimal deep neural network (ODNN) to classify lung cancer in CT lung images. The LDA reduced the extracted image features to minimize the feature dimension. The ODNN was applied and optimized by a modified gravitational search algorithm to provide a more accurate classification result. Compared to CT, LDCT is more sensitive to early-stage lung nodules and cancer detection with reduced radiation. However, it does not help reduce lung cancer mortality. It is recommended that LDCT be carried out annually for high-risk smokers aged 55 to 74 [ 23 ].

PET produces much higher sensitivity and specificity for lung nodule detection than CT due to reactive or granulomatous nodal disease [ 24 ]. PET offers a good correlation with longer progression times and overall survival rates. 18F-FDG PET has been applied to diagnose solitary pulmonary nodules [ 25 ]. 18F-FDG PET is a crucial in-patient selection and advanced NSCLC for radical radiotherapy. PET-assisted radiotherapy offers more accuracy [ 26 ] and manages about 32% of patients with stage IIIA lung cancer [ 27 ]. 18F-FDG PET provides a significant response assessment in patients with NSCLC undergoing induction chemotherapy.

MRI is the most potent lung imaging tool without ionizing radiation, but it provides insufficient information with high costs and time-consuming limitations. It failed to detect about 10% of small lung nodules (4–8 mm in diameter) [ 28 ]. MRI with ultra-short echo time (UTE) can improve signal intensity and reduce lung susceptibility artifacts. MRI with UTE is sensitive for detecting small lung nodules (4–8 mm) [ 29 ]. MRI achieves a higher lung nodule detection rate than LDCT. MRI with different pulse sequences also improved lung nodule detection sensitivity. The authors investigated T1-weighted and T2-weighted MRI to detect small lung nodules [ 30 , 31 ]. Compared to 3T 1.5 MRI, 1.5T MRI is much easier to identify ground glass opacities [ 32 ]. Ground glass opacities were successfully detected in 75% of subjects with lung fibrosis who received 1.5T MRI with SSFP sequences [ 33 ]. MRI with T2-weighted fast spin echo provides similar or even better performance for ground glass infiltrate detection in immunocompromised subjects [ 34 ].

Several research groups have recently investigated the feasibility of using MIT for lung disease detection [ 35 , 36 ]. However, due to the lack of measurement systems, expensive computational electromagnetic models, low image resolution, and some other challenges, MIT technology still has a long way to go before it can be widely used as a commercial imaging tool in clinical conditions.

Medical imaging approaches play an essential strategy in early-stage lung cancer detection and improve the survival rate. However, these techniques have some limitations, including high false positives, and cannot detect lesions automatically. Several CAD systems have been developed for lung cancer detection [ 37 , 38 ]. As shown in Figure 1 , a CAD-based lung nodule detection system [ 14 ] usually consists of three main phases: data collection and pre-processing, training, and testing. There are two types of CAD systems: the detection system identifies specific anomalies according to interest regions, and the diagnostic system analyses lesion information, such as type, severity, stage, and progression.

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CAD-based lung cancer detection system [ 14 ]. The figure is reused from reference [ 14 ]; no special permission is required to reuse all or part of articles published by MDPI, including figures and tables. For articles published under an open-access Creative Common CC BY license.

3. Deep Learning-Based Imaging Techniques

A deep learning-based CAD system has been reported as a promising tool for the automatic diagnosis of lung disease in medical imaging with significant accuracy [ 34 , 35 , 36 ]. The deep learning model is a neural network model with multiple levels of data representation. The deep learning approaches can be grouped into unsupervised, reinforcement, and supervised learning.

Unsupervised learning does not require user guidance, which analyzes the data and then sorts inherent similarities between the input data. Therefore, semi-supervised learning is a mixed model that can provide a win-win situation, even with different challenges. Semi-supervised learning techniques use both labeled and unlabeled data. With the help of labeled and unlabeled data, the accuracy of the decision boundary becomes much higher. Auto-Encoders (AE), Restricted Boltzmann Machines (RBM), and Generative Adversarial Networks (GAN) are good at clustering and nonlinear dimensionality reduction. A large amount of labeled data is usually required during training, which increases cost, time, and difficulty. Researchers have applied deep clustering to reduce labeling and make a more robust model [ 39 , 40 ].

Convolutional neural networks (CNN), deep convolutional neural networks (DCNN), and recurrent neural networks (RNN) are the most widely used unsupervised learning algorithms in medical images. CNN architecture is one of the most widely used supervised deep learning approaches for lesion segmentation and classification because less pre-processing is required. CNN architectures have recently been applied to medical images for image segmentation (such as Mask R-CNN [ 41 ]) and classification (such as AlexNet [ 42 ] and VGGNet [ 43 ]). DCNN architectures usually contain more layers with complex nonlinear relationships, which have been used for classification and regression with reasonable accuracy [ 44 , 45 , 46 ]. RNN architecture is a higher-order neural network that can accommodate the network output to re-input [ 47 ]. RNN applies the Elman network with feedback links from the hidden layer to the input layer, which has the potential to capture and exploit cross-slice variations to incorporate volumetric patterns of nodules. However, RNN has a vanishing gradient problem.

The reinforcement learning technique was first applied in Google Deep Mind in 2013 [ 48 ]. Since then, reinforcement learning approaches have been extensively investigated to improve lung cancer detection accuracy, sensitivity, and specificity. Semi-supervised learning approaches, such as deep reinforcement learning and generative adversarial networks, use labeled datasets.

Supervised learning usually involves a learning algorithm, and labels are assigned to the input data according to the labeling data during training. Various supervised deep learning approaches have been applied to CT images to identify abnormalities with anatomical localization. These approaches have some drawbacks, such as the large amount of labeled data required during training, the assumption of fixed network weights upon training completion, and the inability to be improved after training. Thus, a few-shot learning (FSL) model is developed to reduce data requirements during training.

4. Lung Cancer Prediction Using Deep Learning

This section presents recent achievements in lung cancer and nodule prediction using deep learning techniques. The processing includes image pre-processing, lung nodule segmentation, detection, and classification.

4.1. Imaging Pre-Processing Techniques and Evaluation

4.1.1. pre-processing techniques.

The pre-processed images are injected into a deep learning algorithm with specific architecture and training and tested on the image datasets. The image noise affects the precision of the final classifier. Several noise reduction approaches, such as median filter [ 48 ], Wiener filter [ 49 ], and non-local means filter [ 50 ], have been developed for pre-processing to improve accuracy and generalization performance. After denoising, a normalization method, such as min-max normalization, is required to rescale the images and reduce the complexity of image datasets.

4.1.2. Performance Metrics

Several performance metrics have been used to evaluate the performance of deep learning algorithms, including accuracy, precision, sensitivity, specificity, F1_score, error, mean squared error (MSE), receiver operation characteristic (ROC) curve, over-segmentation rate (OR), under-segmentation rate (UR), Dice similarity coefficient (DSC), Jaccard Score (JS), average symmetric surface distance (ASD), modified Hausdorff distance (MHD), and intersection over union (IoU).

Accuracy assesses the capability concerning the results with the existing information features. Sensitivity is helpful for evaluation when FN is high. Precision is an effective measurement index when FP is high. The F1_score is applied when the class distribution is uneven. ROC can tune detection sensitivity. The area under the receiver operating characteristic curve (AUC) has been used to evaluate the proposed deep learning model. Larger values of accuracy, precision, sensitivity, specificity, AUC, DSC, and JS, and smaller values of Error, UR, OR, and MHD indicate better performance of a deep learning-based algorithm.

These performance metrics can be computed using the following equations [ 51 , 52 ]:

where TP (true positive) denotes the number of correct positives; TN (true negative) indicates the number of correct negatives; FP (false positive) means the number of incorrect positives; FN (false negative) denotes the number of incorrect negatives; B is the target object region, A denotes ground truth dataset, and N a is the number of pixels in A; IoU refers to the percentage of the intersection to the union of the ground truth and predicted areas and is a metric for various object detection and semantic segmentation problems.

4.2. Datasets

Lung image datasets play an essential role in evaluating the performance of deep learning-based algorithms for lung nodule classification and detection. Table 1 shows publicly available lung images and clinical datasets for assessing nodule classification and detection performance.

Lung image dataset.

Reference	Dataset	Sample Number
[ ]	Lung image database consortium (LIDC)	399 CT images
[ ]	Lung image database consortium and image database resource initiative (LIDC-IDRI)	1018 CT images from 1010 patients
[ ]	Lung nodule analysis challenge 2016 (LUNA16)	888 CT images from LIDC-IDRI dataset
[ ]	Early lung cancer action program (ELCAP)	50 LDCT lung images & 379 unduplicated lung nodule CT images
[ ]	Lung Nodule Database (LNDb)	294 CT images from Centro Hospitalar e Universitario de São Joãao
[ ]	Indian Lung CT Image Database (ILCID)	CT images from 400 patients
[ ]	Japanese Society of Radiological Technology (JSRT)	154 nodules & 93 nonnodules with labels
[ ]	Nederland-Leuvens Longkanker Screenings Onderzoek (NELSON)	CT images from 15,523 human subjects
[ ]	Automatic nodule detection 2009 (ANODE09)	5 examples & 50 test images
[ ]	Shanghai Zhongshan hospital database	CT images from 350 patients
[ ]	Society of Photo-Optical Instrumentation Engineers in conjunction with the American Association of Physicists in Medicine and the National Cancer Institute (SPIE-AAPM-NCI) LungX	60 thoracic CT scans with 73 nodules
[ ]	General Hospital of Guangzhou military command (GHGMC) dataset	180 benign & 120 malignant lung nodules
[ ]	First Affiliated Hospital of Guangzhou Medical University (FAHGMU) dataset	142 T2-weighted MR images
[ ]	Non-small cell lung cancer (NSCLC)-Radiomics database	13,482 CT images from 89 patients
[ ]	Danish lung nodule screening trial (DLCST)	CT images from 4104 subjects
[ ]	U.S. National Lung Screening Trial (NLST)	CT images from 1058 patients with lung cancer & 9310 patients with benign lung nodules

4.3. Lung Image Segmentation

Image segmentation aims to recognize the voxel information and external contour of the region of interest. In medical imaging, segmentation is mainly used to segment organs or lesions to quantitatively analyze relevant clinical parameters and provide further guidance for follow-up diagnosis and treatment. For example, target delineation is crucial for surgical image navigation and tumor radiotherapy guidance.

Lung segmentation plays a crucial role in medical images for lesion detection, including thorax extraction (removes artifacts) and lung extraction (identifies the left and right lungs). Several threshold techniques, such as the threshold method [ 69 ], iterative threshold [ 70 ], Otsu threshold [ 71 ], and adaptive threshold [ 72 , 73 ], have been investigated for lung segmentation. Few research groups have investigated segmentation methods based on region and 3D region growth [ 74 , 75 ]. Kass et al. [ 76 ] first introduced the active contour model, and Lan et al. [ 77 ] applied the active contour model for lung segmentation. These techniques are manual segmentation and have many disadvantages, such as being relatively slow, prone to human error, scarcity of ground truth, and class imbalance.

Several deep learning approaches have been investigated for lung segmentation. Wang et al. [ 78 ] developed a multi-view CNN (MV-CNN) for lung nodule segmentation, with an average DSC of 77.67% and an average ASD of 0.24 for the LIDC-IDRI dataset. Unlike conventional CNN, MV-CNN integrates multiple input images for lung nodule identification. However, it is difficult for MV-CNN to process 3D CT scans. Thus, a 3D CNN was developed to process volumetric patterns of cancerous nodules [ 79 ]. Sun et al. [ 80 ] designed a two-stage CAD system to segment lung nodules and FP reduction automatically. The first stage aims to identify and segment the nodules, and the second stage aims to reduce FP. The system was tested on the LIDC-IDRI dataset and evaluated by four experienced radiologists. The system obtained an average F1_score of 0.8501 for lung nodule segmentation.

In 2020, Cao et al. [ 81 ] developed a dual-branch residual network (DB-ResNet) that simultaneously captures the multi-view and multi-scale features of nodules. The proposed DB-ResNet was evaluated on the LIDC-IDRI dataset and achieved a DSC of 82.74%. Compared to trained radiologists, DB-ResNet provides a higher DSC.

In 2021, Banu et al. [ 82 ] proposed an attention-aware weight excitation U-Net (AWEU-Net) architecture in CT images for lung nodule segmentation. The architecture contains two stages: lung nodule detection based on fine-tuned Faster R-CNN and lung nodule segmentation based on the U-Net with position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE). The AWEU-Net obtained DSC of 89.79% and 90.35%, IoU of 82.34%, and 83.21% for the LUNA16 and LIDC-IDRI datasets, respectively.

Dutta [ 83 ] developed a dense recurrent residual CNN (Dense R2Unet) based on the U-Net and dense interconnections. The proposed method was tested on a lung segmentation dataset, and the results showed that the Dense R2UNet offers better segmentation performance than U-Net and ResUNet.

Table 2 shows the recently developed lung nodule segmentation techniques. Among these approaches, SVM systems obtained an accuracy range of 92.6–98.1%, CNN-based systems obtained a specificity range of 77.67–91%, ResNet models obtained a DSC range of 82.74–98.1%, and U-Net segmentation systems achieved an accuracy range of 82.2–99.27%, precision range of 46.61–98.2%, recall range of 21.43–96.33%, and F1_score range of 24.64–99.1%, respectively. The DenseNet201 system obtained an accuracy of 97%, a sensitivity of 96.2%, a specificity of 97.5%, an AUC of 0.968, and an F1_score of 96.1%. Several segmentation methods, including SVM, Dense R2UNet, 3D Attention U-Net, Dense R2UNet, Res BCDU-Net, U-Net FSL, U-Net CT, U-Net PET, U-Net PET/CT, CNN, and DenseNet201, achieved high accuracy results (over 94%).

Lung nodule segmentation approaches.

Reference	Year	Method	Imaging	Datasets	Results
[ ]	2013	Support vector machine (SVM)	CT images	Shiraz University of Medical Sciences	Accuracy: 98.1%
[ ]	2014	Lung nodule segmentation	CT images	85 patients	Accuracy: >90%
[ ]	2015	SVM	CT images	193 CT images	Accuracy: 94.67% for benign tumors; Accuracy: 96.07% for adhesion tumor
[ ]	2015	Bidirectional chain coding combined with SVM	CT images	LIDC	Accuracy: 92.6%
[ ]	2015	Convolutional networks (ConvNets)	CT images	82 patients	DSC: 68% ± 10%
[ ]	2017	Multi-view convolutional neural networks (MV-CNN)	CT images	LIDC-IDRI	DSC: 77.67%
[ ]	2017	Two-stage CAD	CT images	LIDC-IDRI	F1-score: 85.01%
[ ]	2017	3D Slicer chest imaging platform (CIP)	CT images	LIDC	median DSC: 99%
[ ]	2017	Deep computer aided detection (CAD)	CT images	LIDC-IDRI	Sensitivity: 88%
[ ]	2018	3D deep multi-task CNN	CT images	LUNA16	DSC: 91%
[ ]	2018	Improved U-Net	CT images	LUNA16	DSC: 73.6%
[ ]	2018	Incremental-multiple resolution residually connected network (MRRN)	CT images	TCIA	DSC: 74% ± 0.13
				MSKCC	DSC: 75%±0.12
				LIDC	DSC: 68%±0.23
[ ]	2018	U-Net	hematoxylin-eosin-stained slides	712 lung cancer patients operated in Uppsala Hospital, Stanford TMA cores	Precision: 80%
[ ]	2019	Mask R-CNN	CT images	LIDC-IDRI	Average precision:78%
[ ]	2020	3D-UNet	CT images	LUNA16	DSC: 95.30%
[ ]	2020	Dual-branch Residual Network (DB-ResNet)	CT images	LIDC-IDRI	DSC: 82.74%
[ ]	2021	End-to-end deep learning	CT images	1916 lung tumors in 1504 patients	Sensitivity: 93.2%
[ ]	2021	3D Attention U-Net	COVID-19 CT images	Fifth Medical Center of the PLA General Hospital	Accuracy: 94.43%
[ ]	2021	Improved U-Net	CT images	LIDC-IDRI	Precision: 84.91%
[ ]	2021	Attention-aware weight excitation U-Net (AWEU-Net)	CT images	LUNA16	DSC: 89.79%
[ ]	2021	Attention-aware weight excitation U-Net (AWEU-Net)	CT images	LIDC-IDRI	DSC: 90.35%
[ ]	2021	Dense Recurrent Residual Convolutional Neural Network(Dense R2U CNN)	CT images	LUNA	Sensitivity: 99.4% ± 0.2%
[ ]	2021	Modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (Res BCDU-Net)	CT images	LIDC-IDRI	Accuracy: 97.58%
[ ]	2021	Hybrid COVID-19 segmentation and recognition framework (HMB-HCF)	X-Ray images	COVID-19 dataset from 8 sources *	Accuracy: 99.30%
[ ]	2021	Clinical image radionics DL (CIRDL)	CT Images	First Affiliated Hospital of Guangzhou Medical University	Sensitivity: 0.8763
[ ]	2021	2D & 3D hybrid CNN	CT scans	260 patients with lung cancer treated	Median DSC: 0.73
[ ]	2022	Few-shot learning U-Net (U-Net FSL)	PET/CT images	Lung-PET-CT-DX TCIA	Accuracy: 99.27% ± 0.03
		U-Net CT			Accuracy: 99.08% ± 0.05
		U-Net PET			Accuracy: 98.78% ± 0.06
		U-Net PET/CT			Accuracy: 98.92% ± 0.09
		CNN			Accuracy: 98.89% ± 0.08
		Co-learning			Accuracy: 99.94% ± 0.09
[ ]	2022	DenseNet201	CT images	Seoul St. Mary’s Hospital dataset	Sensitivity: 96.2%

COVID-19 dataset from 8 sources *: COVID-19 Radiography Database, Pneumonia (virus) vs. COVID-19 Dataset, Covid-19 X-Ray images using CNN Dataset, COVID-19 X-ray Images5 Dataset, COVID-19 Patients Lungs X-Ray Images 10,000 Dataset, COVID-19 Chest X-Ray Dataset, COVID-19 Dataset, Curated Chest X-Ray Image Dataset for COVID-19.

4.4. Lung Nodule Detection

Lung nodule detection is challenging because its shape, texture, and size vary greatly, and some non-nodules, such as blood vessels and fibrosis, have a similar appearance to lung nodules that often appear in the lungs. The processing includes two main steps: lung nodule detection and false-positive nodule reduction. Over the past few decades, researchers worldwide have extensively investigated machine learning and deep learning-based approaches for lung nodule detection. Chang et al. [ 106 ] applied the support vector machine (SVM) for nodules classification in ultrasound images. Nithila et al. [ 107 ] developed a lung nodule detection model based on heuristic search and particle clustering algorithms for network optimization. In 2005, Zhang et al. [ 108 ] developed a discrete-time cellular neural network (DTCNN) to detect small (2–10 mm) juxtapleural and non-pleural nodules in CT images. The method obtained a sensitivity of 81.25% at 8.29 FPs per scan for juxtapleural nodule detection and a sensitivity of 83.9% at 3.47 FPs per scan for non-pleural nodule detection.

Hwang et al. [ 109 ] investigated the relationship between CT and commercial CAD to detect lung nodules. They also studied LDCT images with three reconstruction kernels (B, C, and L) from 36 human subjects. The sensitivities of 82%, 88%, and 82% for the nodules of B, C, and L were obtained for all images. Experimental results showed that CAD sensitivity could be elevated by combining data from 2 different kernels without radiation exposure. Young et al. [ 110 ] studied the effects on the performance of a CAD-based nodule detection model by reducing the CT dose. The CAD system was evaluated on the NLST dataset and obtained sensitivities of 35%, 20%, and 42.5% at the initial dose, 50% dose, and 25% dose, respectively. Tajbakhsh et al. [ 111 ] studied massive training ANN (MTANN) and CNN for lung nodule detection and classification. MTANN and CNN obtained AUCs of 0.8806 and 0.7755, respectively. MTANN performs better than CNN for lung nodule detection and classification.

Liu et al. [ 112 ] developed a cascade CNN for lung nodule detection. The transfer learning model was applied to train the network to detect nodules, and a non-nodule filter was introduced to the detection network to reduce false positives (FP). The proposed architecture effectively reduces FP in the lung nodule detection system. Li et al. [ 65 ] developed a lung nodule detection method based on a faster R-CNN network and an FP reduction model in thoracic MR images. In this study, a faster R-CNN was developed to detect lung nodules, and an FP reduction model was developed to reduce FP. The method was tested on the FAHGMU dataset and obtained a sensitivity of 85.2%, with 3.47 FP per scan. Cao et al. [ 113 ] developed a two-stage CNN (TSCNN) model for lung nodule detection. In the first stage, a U-Net based on ResDense was applied to detect lung nodules. A 3D CNN-based ensemble learning architecture was proposed in the second stage to reduce false-positive nodules. The proposed model was compared with three existing models, including 3DDP-DenseNet, 3DDP-SeResNet, and 3DMBInceptionNet.

Several 3D CNN models have been developed for lung nodule detection [ 114 , 115 , 116 ]. Perez et al. [ 117 ] developed a 3D CNN to automatically detect lung cancer and tested the model on the LIDC-IDRI dataset. The experimental results showed that the proposed method provides a recall of 99.6% and an AUC of 0.913. Vipparla et al. [ 118 ] proposed a multi-patched 3D CNN with a hybrid fusion architecture for lung nodule detection with reduced FP. The method was tested on the LUNA16 dataset and achieved a competition performance metric (CPM) of 0.931. Dutande et al. [ 119 ] developed a 2D–3D cascaded CNN architecture and compared it with existing lung nodule detection and segmentation methods. The results showed that the 2D–3D cascaded CNN architecture obtained a DCM of 0.80 for nodule segmentation and a sensitivity of 90.01% for nodule detection. Luo et al. [ 120 ] developed a 3D sphere representation-based center-point matching detection network (SCPM-Net) consisting of sphere representation and center-point matching components. The SCPM-Net was tested on the LUNA16 dataset and achieved an average sensitivity of 89.2% at 7 FPs per image for lung nodule detection. Franck et al. [ 121 ] investigated the effects on the performance of deep learning image reconstruction (DLIR) techniques on lung nodule detection in chest CT images. In this study, up to 6 artificial nodules were located within the lung phantom. Images were generated using 50% ASIR-V and DLIR with low (DL-L), medium (DL-M), and high (DL-H) strengths. No statistically significant difference was obtained between these methods ( p = 0.987, average AUC: 0.555, 0.561, 0.557, and 0.558 for ASIR-V, DL-L, DL-M, and DL-H).

Table 3 shows recently developed lung nodule detection approaches using deep learning techniques. Among these approaches, the co-learning feature fusion CNN obtained the best accuracy of 99.29%, which is higher than other lung nodule detection approaches. Several networks, including 3D Faster R-CNN with U-Net-like encoder, YOLOv2, YOLOv3, VGG-16, DTCNN-ELM, U-Net++, MIXCAPS, and ProCAN, obtained good accuracy (>90%) of lung nodule detection.

Lung nodule detection approaches.

Reference	Year	Method	Imaging	Datasets	Results
[ ]	2016	3D CNN	CT images	LUNA16	Sensitivity: >87% at 4 FPs/scan
[ ]	2016	2D multi-view convolutional networks (ConvNets)	CT images	LIDC-IDRI	Sensitivity: 85.4% at 1 FPs/scan, 90.1% at 4 FPs/scan
[ ]	2016	Thresholding method	CT images	JSRT	Accuracy: 96%
[ ]	2017	Computer aided detection (CAD)	LDCT	NLST	Mean sensitivity: 74.1%
[ ]	2017	3D CNN	LDCT	KDSB17	Accuracy: 87.5%
[ ]	2017	3D Faster R-CNN with U-Net-like encoder	CT scans	LUNA16	Accuracy: 81.41%;
[ ]	2017	3D Faster R-CNN with U-Net-like encoder	CT scans	LIDC-IDRI	Accuracy: 90.44%
[ ]	2018	Single-view 2D CNN	CT scans	LUNA16	metric score: 92.2%
[ ]	2018	DetectNet	CT scans	LIDC	Sensitivity: 89%
[ ]	2018	3D CNN	CT scans	KDSB17	Sensitivity: 87%;
[ ]	2018	Novel pulmonary nodule detection algorithm (NODULe) based on 3D CNN	CT scans	LUNA16	CPM score: 94.7%
[ ]	2018		CT scans	LIDC-IDRI	Sensitivity: 94.9%
[ ]	2018	Deep neural networks (DNN)	PET images	50 lung cancer patients, & 50 patients without lung cancer	Sensitivity: 95.9%
[ ]	2018	Deep neural networks (DNN)	ultralow dose PET	50 lung cancer patients, & 50 patients without lung cancer	Sensitivity: 91.5%
[ ]	2018	FissureNet	3DCT	COPDGene	AUC: 0.98
		U-Net			AUC: 0.963
		Hessian			AUC: 0.158
[ ]	2018	DFCN-based cosegmentation (DFCN-CoSeg)	CT scans	60 NSCLC patients	Score: 0.865 ± 0.034;
[ ]	2018	DFCN-based cosegmentation (DFCN-CoSeg)	PET images	60 NSCLC patients	Score: 0.853 ± 0.063;
[ ]	2018	Multi-scale Gradual Integration CNN (MGI-CNN)	CT scans	LUNA16, V1 dataset includes 551,065 subjects; V2 dataset includes 754,975 subjects	CPM: 0.908 for the V1 dataset, 0.942 for the V2 dataset;
[ ]	2018	Deep fully CNN (DFCNet)	CT scans	LIDC-IDR	Accuracy: 84.58%
[ ]	2018	CNN	CT scans	LIDC-IDR	Accuracy: 77.6%
[ ]	2018	Deep learning–based automatic detection algorithm (DLAD)	CT scans	Seoul National University Hospital	Sensitivity: 69.9%
[ ]	2018	SVM classifier coupled with a least absolute shrinkage and selection operator (SVM-LASSO)	CT scans	LIDC-IDRI	Accuracy: 84.6%
[ ]	2019	CNN	CT scans	LIDC-IDR	Sensitivity: 88% at 1.9 FPs/scan; 94.01% at 4.01 FPs/scan
[ ]	2019	3D CNN	LDCT	LUNA16 and Kaggle datasets	Average metric: 92.1%
[ ]	2019	Deep learning model (DLM) based on DCNN	Chest radiographs (CXRs)	3500 CXRs contain lung nodules & 13,711 normal CXRs	Sensitivity: 76.8%
[ ]	2019	Two-Step Deep Learning	CT scans	Nagasaki University Hospital	Sensitivity of 79.6% with sizes ≤0.6 mm; Sensitivity of 75.5% with sizes ≤0.7 mm;
[ ]	2019	Faster R-CNN network and false positive (FP)	CT scans	FAHGMU	Sensitivity: 85.2%
[ ]	2019	YOLOv2 with Asymmetric Convolution Kernel	CT scans	LIDC-IDRI	Sensitivity: 94.25%
[ ]	2019	VGG-16 network	CT scans	LIDC-IDRI	Accuracy: 92.72%
[ ]	2019	Noisy U-Net (NU-Net)	CT scans	LUNA16	Sensitivity: 97.1%
[ ]	2019	CAD using a multi-scale dot nodule-enhancement filter	CT scans	LIDC	Sensitivity: 87.81%
[ ]	2019	Co-Learning Feature Fusion CNN	PET-CT scans	50 NSCLC patients	Accuracy: 99.29%
[ ]	2019	Convolution networks with attention feedback (CONAF)	Chest radiographs	430,000 CXRs	Sensitivity: 78%
[ ]	2019	Recurrent attention model with annotation feedback (RAMAF)	Chest radiographs	430,000 CXRs	Sensitivity: 74%
[ ]	2020	Two-Stage CNN (TSCNN)	CT scans	LUNA16 & LIDC-IDRI	CPM: 0.911
[ ]	2020	Deep Transfer CNN and Extreme Learning Machine (DTCNN-ELM)	CT scans	LIDC-IDRI & FAH-GMU	Sensitivity: 93.69%;
[ ]	2020	U-Net++	CT scans	LIDC-IDRI	Sensitivity: 94.2% at 1 FP/scan, 96% at 2 FPs/scan
[ ]	2020	MSCS-DeepLN	CT scans	LIDC-IDRI & DeepLN
[ ]	2020	Multi-scale CNN (MCNN)	CT scans	LIDC-IDRI	Accuracy: 93.7% ± 0.3
[ ]	2021	Lung Cancer Prediction CNN (LCP-CNN)	CT scans	U.S. NLST	Sensitivity: 99%;
[ ]	2021	Automatic AI-powered CAD	CT scans	150 images include 340 nodules	mean sensitivity: 82% for second-reading mode, 80% for concurrent-reading mode
[ ]	2021	DNA-derived phage nose (D2pNose) using machine learning and ANN	CT scans	Pusan National University	Detection accuracy: >75%; Classification accuracy: >86%
[ ]	2021	Capsule network-based mixture of experts (MIXCAPS)	CT scans	LIDC-IDRI	Sensitivity: 89.5%;
[ ]	2021	CNN with attention mechanism	CT scans	LUNA16	Specificity: 98.9%
[ ]	2021	Deep learning image reconstruction (DLIR)	CT scans	LIDC-IDRI	AUC of 0.555, 0.561, 0.557, 0.558 for ASIR-V, DL-L, DL-M, DL-H
[ ]	2021	2D-3D cascaded CNN	CT scans	LIDC-IDRI	Sensitivity: 90.01%
[ ]	2022	3D sphere representation-based center-points matching detection network (SCPM-Net)	CT scans	LUNA16	Average sensitivity: 89.2%
[ ]	2022	YOLOv3	CT scans	RIDER	Accuracy: 95.17%
[ ]	2022	3D Attention CNN	CT scans	LUNA16	CPM: 0.931
[ ]	2022	Progressive Growing Channel Attentive Non-Local (ProCAN) network	CT scans	LIDC-IDRI	Accuracy: 95.28%

4.5. Lung Nodule Classification

In recent years, investigators have studied various deep learning techniques to improve the performance of lung nodule classification [ 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 ]. The sensitivity and specificity of the SIFT-based classifier and SVM in the classification of pulmonary nodules reached 86% and 97% [ 160 ], 91.38%, and 89.56% [ 163 ], respectively. The accuracy, sensitivity, and specificity of multi-scale CNN and multi-crop CNN in lung nodule classification were 90.63%, 92.30%, and 89.47% [ 164 ], respectively, and 87%, 77%, and 93% [ 170 ], respectively. The accuracy of deep-level semantic networks and multi-scale CNN in lung nodule classification were 84.2% [ 167 ] and 86.84% [ 168 ], respectively. The CAD system developed by Cheng et al. [ 169 ] achieved the best accuracy of 95.6%, sensitivity of 92.4%, and specificity of 98.9% in the classification of pulmonary nodules.

The comparative study results showed that the sensitivity and specificity of CNN and DBN for pulmonary nodule classification are 73.40% and 73.30%, 82.20%, and 78.70%, respectively [ 165 ]. Another comparative study showed that the sensitivity and specificity of CNN and ResNet in the classification of nodules are 76.64% and 89.50%, 81.97%, and 89.38%, respectively [ 171 ]. The combined application of CNN and RNN achieved accuracy, sensitivity, and specificity of 94.78%, 94.66%, and 95.14%, respectively, in classifying pulmonary nodules [ 172 ].

In 2019, Zhang et al. [ 174 ] used an ensemble learner of multiple deep CNN in CT images and obtained a classification accuracy of 84% for the LIDC-IDRI dataset. The proposed classifier achieved better performance than other algorithms, such as SVM, multi-layer perceptron, and random forests.

Sahu et al. [ 175 ] proposed a lightweight multi-section CNN with a classification accuracy of 93.18% for the LIDC-IDRI dataset to improve accuracy. The proposed architecture could be applied to select the representative cross sections determining malignancy that facilitate the interpretation of the results.

Ali et al. [ 176 ] developed a system based on transferable texture CNN that consists of nine layers to extract features automatically and classify lung nodules. The proposed method achieved an accuracy of 96.69% ± 0.72%, with an error of 3.30% ± 0.72% and a recall of 97.19% ± 0.57%, respectively.

Marques et al. [ 177 ] developed a multi-task CNN to classify malignancy nodules with an AUC of 0.783. Thamilarasi et al. [ 178 ] proposed an automatic lung nodule classifier based on CNN with an accuracy of 86.67% for the JSRT dataset. Kawathekar et al. [ 179 ] developed a lung nodule classifier using a machine-learning technique with an accuracy of 94% and an F1_score of 92% for the LNDb dataset.

More recently, Radford et al. [ 180 ] proposed deep convolution GAN (DCGAN), Chuquicusma et al. [ 181 ] applied DCGAN to generate realistic lung nodules, and Zhao et al. [ 182 ] applied Forward and Backward GAN (F&BGAN) to classify lung nodules. The F&BGAN was evaluated on the LIDC-IDRI dataset and obtained the best accuracy of 95.24%, a sensitivity of 98.67%, a specificity of 92.47%, and an AUC of 0.98.

Table 4 shows the recently developed traditional and deep learning-based techniques for classifying lung nodules. Among these methods, CNN variants obtained an accuracy range of 83.4–99.6%, a specificity range of 73.3–95.17%, a sensitivity range of 73.3–96.85%, and an AUC range of 0.7755–0.9936, respectively. Several methods achieved high classification accuracy (>95%), including F&BGAN, Inception_ResNet_V2, ResNet152V2, ResNet152V2+GRU, CSO-CADLCC, ProCAN, Net121, ResNet50, DITNN, and optimal DBN with an opposition-based pity beetle algorithm. DCNN systems obtained a sensitivity of 89.3% [ 183 ] and an accuracy of 97.3% [ 184 ]. The classifier was developed based on the VGG19 and CNN models and achieved accuracy, sensitivity, specificity, recall, F1_score, AUC, and MCC above 98%.

Lung nodule classification approaches.

Reference	Year	Method	Imaging	Datasets	Results
[ ]	2014	FF-BPNN	CT scans	LIDC	Sensitivity: 91.4%
[ ]	2015	Multi-scale CNN	CT scans	LIDC-IDRI	Accuracy: 86.84%
[ ]	2015	CAD using deep features	CT scans	LIDC-IDRI	Sensitivity: 83.35%
[ ]	2015	Deep belief network (DBN)	CT scans	LIDC	Sensitivity: 73.4%
[ ]	2015	CNN	CT scans	LIDC	Sensitivity:73.3%
[ ]	2015	Fractal	CT scans	LIDC	Sensitivity:50.2%
[ ]	2015	Scale-invariant feature transform (SIFT)	CT scans	LIDC	Sensitivity: 75.6%
[ ]	2016	Intensity features +SVM	CT scans	DLCST	Accuracy: 27.0%
[ ]	2016	Unsupervised features+SVM	CT scans	DLCST	Accuracy: 39.9%
[ ]	2016	ConvNets 1 scale	CT scans	DLCST	Accuracy: 84.4%
[ ]	2016	ConvNets 2 scale	CT scans	DLCST	Accuracy: 85.6%
[ ]	2016	ConvNets 3 scale	CT scans	DLCST	Accuracy: 85.6%
[ ]	2017	Multi-crop CNN	CT scans	LIDC-IDRI	Accuracy: 87.14%
[ ]	2017	Deep 3D DPN	CT scans	LIDC-IDRI	Accuracy: 88.74%
[ ]	2017	Deep 3D DPN+ GBM	CT scans	LIDC-IDRI	Accuracy: 90.44%
[ ]	2017	Massive-training ANN (MTANN)	CT scans	LDCT	AUC: 0. 8806
[ ]	2017	CNN	CT scans	LDCT	AUC: 0.7755
[ ]	2017	Wavelet Recurrent Neural Network	Chest X-Ray	Japanese Society Radiology and Technology	Sensitivity: 88.24%
[ ]	2017	Multi-crop convolutional neural network (MC-CNN)	CT scans	LIDC-IDRI	Sensitivity: 77%
[ ]	2018	Topology-based phylogenetic diversity index classification CNN	CT scans	LIDC	Sensitivity: 90.70%
[ ]	2018	Transfer learning deep 3D CNN	CT scans	Institution records	Accuracy: 71%
[ ]	2018	CNN	CT scans	Kaggle Data Science Bowl 2017	Sensitivity: 87%
[ ]	2018	Feature Representation Using Deep Autoencoder	CT scans	ELCAP	Accuracy: 93.9%
[ ]	2018	Multi-view multi-scale CNN	CT scans	LIDC-IDRI & ELCAP	overall classification rates: 92.3% for LIDC-IDRI; overall classification rates: 90.3% for ELCAP
[ ]	2018	Wavelet-Based CNN	CT scans	448 images include four categories	Accuracy: 91.9%
[ ]	2018	Deep ConvNets	CT scans	LIDC-IDRI	Accuracy: 98%
[ ]	2018	Forward and Backward GAN (F&BGAN)	CT scans	LIDC-IDRI	Sensitivity: 98.67%
[ ]	2019	Ensemble learner of multiple deep CNN	CT scans	LIDC-IDRI	Accuracy: 84.0%
[ ]	2019	Lightweight Multi-Section CNN	CT scans	LIDC-IDRI	Accuracy: 93.18%
[ ]	2019	Deep hierarchical semantic CNN (HSCNN)	CT scans	LIDC	Sensitivity: 70.5%
[ ]	2019	Multi-view knowledge-based collaborative (MV-KBC)	CT scans	LIDC-IDRI	Accuracy: 91.60%
[ ]	2019	3D CNN	CT scans	LIDC	Sensitivity: 66.8%
[ ]	2019	DCNN	CT scans	46 images from interventional cytology	Sensitivity: 89.3%
[ ]	2019	3D MixNet	CT scans	LIDC-IDRI & LUNA16	Accuracy: 88.83%
[ ]	2019	3D MixNet +GBM	CT scans	LIDC-IDRI & LUNA16	Accuracy: 90.57%
[ ]	2019	3D CMixNet+ GBM	CT scans	LIDC-IDRI & LUNA16	Accuracy: 91.13
[ ]	2019	3D CMixNet+ GBM+Biomarkers	CT scans	LIDC-IDRI & LUNA16	Accuracy: 94.17%
[ ]	2019	Deep Learning with Instantaneously Trained Neural Networks (DITNN)	CT scans	Cancer imaging Archive (CIA)	Accuracy: 98.42%
[ ]	2020	DCNN	CT scans	LIDC	Accuracy: 97.3%
[ ]	2020	CNN	CT scans	LIDC	Sensitivity: 93.4%
[ ]	2020	2.75D CNN	CT scans	LUNA16	AUC: 0.9842
[ ]	2020	Two-step Deep Network (TsDN)	CT scans	LIDC-IDRI	Sensitivity: 88.5%
[ ]	2020	Transferable texture CNN	CT scans	LIDC-IDRI & LUNGx	Accuracy: 96.69% ± 0.72%
[ ]	2020	Taguchi-Based CNN	X-ray & CT images	245,931 images	Accuracy: 99.6%
[ ]	2021	Optimal Deep Belief Network with Opposition-based Pity Beetle Algorithm	CT scans	LIDC-IDRI	Sensitivity: 96.86%
[ ]	2021	Multi-task CNN	CT scans	LIDC-IDRI	AUC: 0.783
[ ]	2021	CNN	CT scans	JSRT	Accuracy: 86.67%
[ ]	2021	Inception_ResNet_V2	CT scans	LC25000	Accuracy: 99.7%
[ ]	2021	VGG19	CT scans	LC25000	Accuracy: 92%
[ ]	2021	ResNet50	CT scans	LC25000	Accuracy: 99%
[ ]	2021	Net121	CT scans	LC25000	Accuracy: 99.4%
[ ]	2021	Improved Faster R-CNN and transfer learning	CT scans	Heilongjiang Provincial Hospital	Accuracy: 89.7%
[ ]	2021	Three-stream network	CT scans	LIDC-IDRI	Accuracy: 98.2%
[ ]	2021	FractalNet	CT scans	LUNA 16	Sensitivity: 96.68%
[ ]	2021	VGG19+CNN	X-ray & CT images	GitHub	Specificity: 99.5%
[ ]	2021	ResNet152V2	X-ray & CT images	GitHub	Specificity: 98.4%
[ ]	2021	ResNet152V2+GRU	X-ray & CT images	GitHub	Specificity: 98.7%
[ ]	2021	ResNet152V2+Bi-GRU	X-ray & CT images	GitHub	Specificity: 97.8%
[ ]	2022	Machine learning	CT scans	LNDb	Accuracy: 94%
[ ]	2022	Progressively Growing Channel Attentive Non-Local (ProCAN)	CT scans	LIDC-IDRI	Accuracy: 95.28%
[ ]	2022	CNN-based multi-task learning (CNN-MTL)	CT scans	LIDC-IDRI	Sensitivity: 96.2%
[ ]	2022	Cat swarm optimization-based CAD for lung cancer classification (CSO-CADLCC)	CT scans	Benchmark	Specificity: 99.17%
[ ]	2022	2-Pathway Morphology-based CNN (2PMorphCNN)	CT scans	LIDC-IDRI	Sensitivity: 96.85%

Forte et al. [ 209 ] recently conducted a systematic review and meta-analysis of the diagnostic accuracy of current deep learning approaches for lung cancer diagnosis. The pooled sensitivity and specificity of deep learning approaches for lung cancer detection were 93% and 68%, respectively. The results showed that AI plays an important role in medical imaging, but there are still many research challenges.

5. Challenges and Future Research Directions

This study extensively surveys papers published between 2014 and 2022. Table 2 , Table 3 and Table 4 demonstrate that deep learning-based lung imaging systems have achieved high efficiency and state-of-the-art performance for lung nodule segmentation, detection, and classification using existing medical images. Compared to reinforcement and supervised learning techniques, unsupervised deep learning techniques (such as CNN, Faster R-CNN, Mask R-CNN, and U-Net) are more popular methods that have been used to develop convolutional networks for lung cancer detection and false-positive reduction.

Previous studies have shown that CT is the most widely used imaging tool in the CAD system for lung cancer diagnosis. Compared to 2D CNN, 3D CNN architectures provide more promising usefulness in obtaining representative features of malignant nodules. To this day, only a few works on 3D CNN for lung cancer diagnosis have been reported.

Deep learning techniques have achieved good performance in segmentation and classification. However, deep learning techniques still have many unsolved problems in lung cancer detection. First, clinicians have not fully acknowledged deep learning techniques for everyday clinical exercise due to the lack of standardized medical image acquisition protocols. The unification of the acquisition protocols could minimize it.

Second, deep learning techniques usually require massive annotated medical images by experienced radiologists to complete training tasks. However, it is costly and time consuming to collect an enormous annotated image dataset, even performed by experienced radiologists. Several methods were applied to overcome the scarcity of annotated data. For example, transfer learning is a possible way to solve the training problem of small samples. Another possible method is the computer synthesis of images, such as the generation of confrontation networks. Inadequate data will inevitably affect the accuracy and stability of predictions. Therefore, improving prediction accuracy using weak supervision, transfer learning, and multi-task learning with small labeled data is one of the future research directions.

Third, the clinical application of deep learning requires high interpretability, but current deep learning techniques cannot effectively explain the learned features. Many researchers have applied visualization and parameter analysis methods to explain deep learning models. However, there is still a certain distance from the interpretable imaging markers required by clinical requirements. Therefore, investigating the interpretable deep learning method will be a hot spot in the medical image field.

Fourth, developing the robustness of the prediction model is a challenging task. Most deep learning techniques work well only for a single dataset. The image of the same disease may vary significantly due to different acquisition parameters, equipment, time, and other factors. This led to poor robustness and generalization of existing deep learning models. Thus, improving the model structure and training methods by combining brain cognitive ideas and improving the generalization ability of deep learning is one of the key future directions.

Finally, some of the current literature has little translation into applicability in clinical practice due to the lack of experience of non-medical investigators in choosing more relevant clinical outcomes. Most deep learning techniques were developed by non-medical professionals with little or no oversight of radiologists, who, in practice, will use these resources when they become more widely available. As a result, some performance metrics, such as accuracy, AUC, and precision, which have little meaningful clinical application, continue to be used and are often the only summary outcomes reported by some studies. Instead, investigators should always strive to report more relevant clinical parameters, such as sensitivity and specificity, because they are independent of the prevalence of the disease and can be more easily translated into practice.

In the future, investigators should pay more attention to the following research directions: (1) develop new convolutional networks and loss functions to improve the performance; (2) weak supervised learning, using a large number of incomplete, inaccurate, and ambiguous annotation data in the existing medical records to achieve model training; (3) bring prior clinical knowledge into model training; (4) radiologists, computer scientists, and engineers need to work more closely to develop more realistic and sensitive models and add more meaning to the research field; (5) single disease identification to complete disease identification. In clinical examination, only a few cases need to solve one well-defined problem. For example, clinicians can detect pulmonary nodules in LDCT and check whether there are other abnormalities, such as emphysema. Solving multiple problems with one network will not reduce performance in specific tasks. In addition, deep learning can be explored in some areas where the medical mechanism is not precise, such as large-scale lung image analysis using deep learning, which is expected to make diagnosing lung diseases more objective.

6. Conclusions

This paper reviewed recent achievements in deep learning-based approaches for lung nodule segmentation, detection, and classification. CNN is one of the most widely used deep learning techniques for lung disease detection and classification, and CT image datasets are the most frequently used imaging datasets for training networks. The article review was based on recent publications (published in 2014 and later). Experimental and clinical trial results demonstrate that deep learning techniques can be superior to trained radiologists. Deep learning is expected to effectively improve lung nodule segmentation, detection, and classification. With this powerful tool, radiologists can interpret images more accurately. Deep learning algorithm has shown great potential in a series of tasks in the radiology department and has solved many medical problems. However, it still faces many difficulties, including large-scale clinical verification, patient privacy protection, and legal accountability. Despite these limitations, with the current trend and rapid development of the medical industry, deep learning is expected to generate a greater demand for accurate diagnosis and treatment in the medical field.

Acknowledgments

The author would like to thank the reviewers for their critical comments to improve the manuscript significantly.

Funding Statement

This research was funded by the International Science and Technology Cooperation Project of the Shenzhen Science and Technology Commission (GJHZ20200731095804014).

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The author declares no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Research News

Hackensack Meridian CDI Lab Publishes Papers Pointing the Way Toward an Exhaled-Breath Test for Lung Cancer

July 18, 2024

Hackensack Meridian Health

CDI scientists publish groundbreaking papers on lung cancer breath test and immune system’s role in fighting diseases. These discoveries could revolutionize cancer detection and treatment, potentially improving patient outcomes.

Scientists at the Center for Discovery and Innovation (CDI) have made exciting progress in cancer research. They’ve published two important papers that could change how we detect and treat cancer.

The first paper talks about a new way to test for lung cancer using a person’s breath. This test could be a game-changer because it’s quick, easy, and doesn’t hurt. The researchers found that certain chemicals in a person’s breath might show if they have lung cancer. This could help doctors find cancer earlier, which is really important for successful treatment.

The second paper is about how our bodies fight off infections and cancer. The scientists discovered that a part of our immune system, called neutrophils, can change to help fight diseases better. This finding could lead to new ways to boost our body’s natural defenses against cancer and infections.

Dr. David Perlin, who leads the CDI, says these discoveries are a big deal. They show how the CDI is working hard to find new ways to help patients. The research team used advanced technology and worked together with other experts to make these breakthroughs.

The breath test for lung cancer is especially exciting because it could be a simple way to check for cancer without invasive procedures. It might even help find other types of cancer in the future.

The study on neutrophils is also important because it gives us new ideas about how to make our immune systems stronger. This could lead to better treatments for both cancer and infections.

Overall, this research is a big step forward in understanding and fighting diseases. It shows how scientists are always working to find new ways to keep us healthy.

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Open access
Published: 02 August 2024

CD98 heavy chain protein is overexpressed in non-small cell lung cancer and is a potential target for CAR T-cell therapy

Moto Yaga 1 , 2 ,
Kana Hasegawa 3 ,
Shunya Ikeda 4 ,
Miwa Matsubara 4 ,
Takashi Hiroshima 5 ,
Toru Kimura 5 ,
Yuya Shirai 1 , 6 ,
Wibowo Tansri 1 ,
Hirofumi Uehara 4 ,
Mana Tachikawa 4 ,
Yuzuru Okairi 7 ,
Masayuki Sone 7 ,
Hiromi Mori 7 ,
Yosuke Kogue 7 ,
Hiroki Akamine 7 ,
Daisuke Okuzaki 8 , 9 ,
Kotaro Kawagishi 10 ,
Satoshi Kawanaka 10 ,
Hiroyuki Yamato 10 ,
Yukiyasu Takeuchi 10 ,
Eiji Okura 11 ,
Ryu Kanzaki 12 ,
Jiro Okami 12 ,
Itsuko Nakamichi 13 ,
Shigeru Nakane 14 ,
Aki Kobayashi 15 ,
Takashi Iwazawa 15 ,
Toshiteru Tokunaga 16 ,
Hideoki Yokouchi 17 ,
Yukihiro Yano 18 ,
Junji Uchida 18 ,
Masahide Mori 18 ,
Kiyoshi Komuta 19 ,
Tetsuro Tachi 20 ,
Hideki Kuroda 20 ,
Noriyuki Kijima 20 ,
Haruhiko Kishima 20 ,
Michiko Ichii 21 ,
Shinji Futami 1 ,
Yujiro Naito 1 ,
Takayuki Shiroyama 1 ,
Kotaro Miyake 1 ,
Shohei Koyama 1 , 22 , 23 ,
Haruhiko Hirata 1 ,
Yoshito Takeda 1 ,
Soichiro Funaki 5 ,
Yasushi Shintani 5 ,
Atsushi Kumanogoh 1 , 2 , 24 , 25 , 26 , 27 &
Naoki Hosen 3 , 21 , 28

Scientific Reports volume 14 , Article number: 17917 ( 2024 ) Cite this article

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Immunotherapy
Lung cancer

Chimeric antigen receptor (CAR) T cells are effective against hematological cancers, but are less effective against solid tumors such as non-small cell lung cancer (NSCLC). One of the reasons is that only a few cell surface targets specific for NSCLC cells have been identified. Here, we report that CD98 heavy chain (hc) protein is overexpressed on the surface of NSCLC cells and is a potential target for CAR T cells against NSCLC. Screening of over 10,000 mAb clones raised against NSCLC cell lines showed that mAb H2A011 bound to NSCLC cells but not normal lung epithelial cells. H2A011 recognized CD98hc. Although CAR T cells derived from H2A011 could not be established presumably due to the high level of H2A011 reactivity in activated T cells, those derived from the anti-CD98hc mAb R8H283, which had been shown to lack reactivity with CD98hc glycoforms expressed on normal hematopoietic cells and some normal tissues, were successfully developed. R8H283 specifically reacted with NSCLC cells in six of 15 patients. R8H283-derived CAR T cells exerted significant anti-tumor effects in a xenograft NSCLC model in vivo. These results suggest that R8H283 CAR T cells may become a new therapeutic tool for NSCLC, although careful testing for off-tumor reactivity should be performed in the future.

Targeting PD-L1 in non-small cell lung cancer using CAR T cells

introduction for lung cancer research paper

Pre-clinical validation of a pan-cancer CAR-T cell immunotherapy targeting nfP2X7

Preclinical development of CD126 CAR-T cells with broad antitumor activity

Introduction.

Non-small cell lung cancer (NSCLC) is one of the most common causes of cancer deaths worldwide 1 , 2 . Although immune checkpoint blockade therapy has largely improved the prognosis of NSCLC 3 , 4 , advanced NSCLC remains incurable in most cases. New therapeutic options, including CAR T cell therapy, are therefore urgently needed for patients with NSCLC. CAR T cell therapy has shown tremendous efficacy in the treatment of hematological cancers 5 , 6 . Although recent reports have demonstrated that CAR T cells exert an anti-tumor effect against some types of solid tumors 7 , 8 , 9 , 10 , they are still less effective against solid tumors, including NSCLC, than against hematological cancers. One of the major reasons is the lack of cell surface target antigens that are specific for tumor cells. Several cell surface antigens, such as epidermal growth factor receptor 11 , 12 , mesothelin 10 , prostate stem cell antigen 13 , mucin 1 13 , human epidermal growth factor receptor 2 14 , carcinoembryonic antigen 15 , 16 , programmed death-ligand 1 17 , and receptor tyrosine-kinase-like orphan receptor 18 , 19 , 20 , have been tested as targets for CAR T cells intended to treat NSCLC, although these target molecules are not completely tumor specific. Identifying additional target antigens specific for NSCLC is important to develop effective and safe CAR T-cell therapy.

Expression levels of messenger RNA were previously shown to lack sufficient correlation with the abundances of their corresponding proteins 21 . In addition, cancer-specific conformational epitopes formed by post-translational events such as glycosylation or conformational changes may have been missed in screening using transcriptome analysis 22 . Previously, we thoroughly screened for multiple myeloma (MM)-specific monoclonal antibodies (mAbs) among large numbers of mAbs raised against MM cells, and identified two novel mAbs recognizing MM-specific antigens that could not be found by transcriptome analysis 23 , 24 . The first was an mAb that specifically recognized the activated integrin β7, which is constitutively overexpressed in MM cells 23 . The second was R8H283, which had been shown to lack reactivity with CD98hc glycoforms expressed on normal hematopoietic cells and also with some normal tissues 24 . In this study, we applied the same strategy to identify NSCLC-specific cell surface targets and found that H2A011, which recognizes the CD98hc protein, reacted specifically with a subset of NSCLC samples, but not with normal lung epithelial cells. Although T cells transduced with H2A011-derived CAR could not be expanded in vitro, T cells transduced with another anti-CD98hc mAb that we previously reported 24 could be expanded and exerted anti-tumor activity in vitro and in vivo.

H2A011 reacted with NSCLC cells but not with normal lung epithelial cells

We immunized mice with one of five NSCLC cell lines (A549, H1792, H1975, H2228, or HCC827) and generated approximately 10,000 clones of mAbs that bound to the cell line used for immunization. Among them, we selected 573 hybridomas that produced mAbs lacking reactivity to Ep-CAM + lung epithelial cells obtained from normal regions of resected lung tissues. Then, Ep-CAM + NSCLC cells from tumor regions of resected specimens were stained with the candidate mAbs and subjected to flow cytometry analysis. We identified 12 candidate mAbs that bound to Ep-CAM + NSCLC cells in at least one sample. Among them, we focused on H2A011, which reacted most frequently with NSCLC cells (Fig. 1 A). Distinct binding of H2A011 to NSCLC cells was observed in four of the five patients with NSCLC, while H2A011 did not react with any of the five samples of normal lung epithelial cells (Fig. 1 B,C, Supplementary Fig. S1 A and B, Supplementary Table S1 ). H2A011 also reacted with all NSCLC cell lines tested (Supplementary Fig. S1 C).

An anti-CD98hc mAb, H2A011, reacts with NSCLC cells but not with normal lung epithelial cells. ( A ), Strategy for identification of the NSCLC-specific mAb H2A011. ( B ) and ( C ) , Flow cytometry analyses of H2A011 reactivity against CD45 − Ep-CAM + normal lung epithelial cells ( B ) and CD45 − Ep-CAM + tumor cells ( C ) from resected NSCLC tissues. Analyses of live (propidium iodide-negative) cells are shown. Results of staining with isotype control instead of anti-Ep-CAM mAb were used to draw the gate for Ep-CAM + cells. The analysis of cells from patient UPN4 is shown as an example. The results of other patients are shown in Supplementary Fig. S1 . ( D ), Strategy for identifying the antigen recognized by H2A011. ( E ), Flow cytometry plots showing the process of enriching H2A011 + cells in expression cloning of the H2A011 antigen. ( F ), Flow cytometry analysis of the binding of H2A011 or MEM-108 (a known anti-CD98hc mAb) to wild-type (WT) or CD98hc-deficient (CD98hc KO) A549 cells.

H2A011 recognized CD98hc

The antigen recognized by H2A011 was identified by expression cloning using retroviruses 25 (Fig. 1 D). Specifically, retroviruses carrying a cDNA library generated from A549 cells (H2A011 positive) were used to infect Ba/F3 cells (H2A011 negative), and then cells labeled with H2A011 were enriched by FACS. After the third round of cell sorting, most cells were H2A011 positive. Sequencing of the inserted cDNA revealed that H2A011 recognized CD98hc (also known as SLC3A2) (Fig. 1 E). Consistent with this, H2A011 reactivity was absent in CD98hc knockout (KO) A549 cells that were established using the CRISPR-Cas9 system and confirmed as CD98hc deficient by lack of staining with the known CD98hc-reactive antibody MEM-108 (Fig. 1 F).

In transcriptome analyses comparing CD45 − CD31 − epithelial cell adhesion molecule (Ep-CAM) + cells 26 from tumor regions with those from normal regions of lung tissues resected from three patients with NSCLC (Supplementary Fig. S2 A, B, Supplementary Table S1 ), CD98hc (SLC3A2) mRNA expression was comparable in two of three patients (Supplementary Fig. S2 C). In addition, we confirmed the expression of CD98hc in publicly available single-cell RNA sequencing data. We extracted the data from Laughney et al., 27 which included the samples from eight lung NSCLC cells and four normal lungs from the Human Lung Cell Atlas dataset 28 . The expression of CD98hc was not significantly different between NSCLC cells and normal lung epithelial cells (Supplementary Fig. S3 ).

Successful development of CAR T cells derived not from H2A011, but from another anti-CD98hc mAb, R8H283, that lacks reactivity with CD98hc glycoforms expressed on normal hematopoietic cells.

Four CAR constructs derived from the variable region of H2A011 were established using either CD28 or 4-1BB as a co-stimulatory molecule (Fig. 2 A). T cells were transduced with each CAR construct and cultured in vitro. However, after 10 d of culture, T cells expressing each CAR were scarcely detected (Fig. 2 B). High levels of H2A011 reactivity in activated T lymphocytes (Fig. 2 C,D) may be a cause of loss of H2A011 CAR T cells.

Successful development of CAR T cells derived not from H2A011, but from another anti-CD98hc mAb, R8H283, that lacks reactivity with CD98hc glycoforms expressed on normal hematopoietic cells. ( A ), Constructs for the CAR derived from H2A011. ( B ), Flow cytometry analysis of H2A011 CAR transduction efficiencies 7 d after CAR transduction. ( C , D ), Flow cytometric analysis of R8H283 or H2A011 reactivity against phytohemagglutinin P (PHA)-activated T cells ( C ) and CD3/CD28-stimulated T cells ( D ). A549 cells were simultaneously stained as a positive control. ( E ), Flow cytometry analysis of the binding of H2A011 or R8H283 to wild-type (WT) or GnTI-deficient (GnTI − ) 293 cells. F, Construct for the CAR derived from R8H283. ( G ), Growth of R8H283 CAR T cells during in vitro culture. The data are presented as means ± standard error of the mean (SEM). *: p < 0.05. ( H ), Representative flow cytometry analysis data of R8H283 CAR transduction efficiencies and CD4/CD8 expression in CAR T cells 7 d after CAR transduction.

Another anti-CD98hc mAb, R8H283, was previously shown to bind myeloma cells but not normal tissues due to differences in CD98hc N-glycosylation 24 . Consistently, R8H283, but not H2A011, reactivity was significantly increased in GnTI-deficient 293cells, which do not have N-acetylglucosaminyltransferase I (GnTI) activity and therefore lack complex N-glycans (Fig. 2 E). A CAR construct derived from the variable region of R8H283 was established using CD28 as a co-stimulatory molecule (Fig. 2 F). T cells were transduced with the CAR construct and cultured in vitro. CAR T cells expressing the R8H283-derived CAR could be expanded, although the expansion of R8H283 CAR T cells was reduced compared to that of control T cells (Fig. 2 G,H). Reactivity of R8H283 was detected in activated T cells but was much lower than that of H2A011 (Fig. 2 C,D). R8H283 CAR T cells spontaneously produced small amounts of cytokines even in the absence of antigen stimulation (Supplementary Fig. S4 ).

R8H283 reacted with NSCLC cells in a subset of patients

R8H283 reacted with CD45 − CD31 − Ep-CAM + NSCLC cells in six of 15 patients (Fig. 3 A,B, Supplementary Fig. S5 A), but did not react with any of the normal lung epithelial cells from ten patients (Fig. 3 C and D and Supplementary Fig. S5 B). These results indicate that R8H283 reactivity is specific for NSCLC cells in a subset of patients. Five of the six NSCLC samples that reacted with R8H283 were squamous cell carcinomas (Fig. 3 A and B , Supplementary Fig. S5 A, Supplementary Table S1 ). In the samples that we were able to analyze in pairs (tumor vs normal epithelial cells), R8H283 reacted with tumor cells but not with normal lung epithelial cells (UPN 7, 10, 11). The results of MEM108 (pan-CD98hc mAb) staining showed that CD98hc protein was expressed at significantly higher levels on NSCLC cells than on normal epithelial cells, hematopoietic cells, and endothelial cells (Fig. 3 E). To further explore the basis for the NSCLC specificity of R8H283, we compared CD98hc in normal lung epithelial cells and NSCLC cells. Whole cell lysates of normal lung epithelial cells or NSCLC cells from a patient were electrophoresed and immunoblotted with polyclonal anti-CD98 antibody (Supplementary Fig. S6 ). The mobility of CD98hc was different in the NSCLC cells compared to normal lung epithelial cells. We also found that the electrophoretic mobilities of the CD98hc species in NSCLC cells and normal lung epithelial cells were still different after the removal of N-glycans by PNGase F treatment. Thus, it was unclear whether the difference in electrophoretic mobility reflected from the difference in glycosylation or the expressed spliced forms.

R8H283 reacted with NSCLC cells in a subset of patients. ( A ), Flow cytometry analyses of R8H283 reactivity against CD45 − CD31 − Ep-CAM + tumor cells in the tumor region of lung tissues resected from a patient with NSCLC (UPN5). ( B ), Representative results of flow cytometry analyses of R8H283 reactivity against CD45 − CD31 − Ep-CAM + tumor cells in the tumor regions of lung tissues resected from patients with NSCLC. Analyses of the other samples are shown in Supplementary Fig. S5 A. ( C ), Flow cytometry analysis of R8H283 reactivity against CD45 − CD31 − Ep-CAM + lung epithelial cells in normal regions of resected lung tissues from a patient with NSCLC (UPN10). ( D ), Representative results of flow cytometry analyses of R8H283 reactivity against CD45 − CD31 − Ep-CAM + lung epithelial cells in unaffected regions of resected lung tissues. Analyses of the other samples are shown in Supplementary Fig. S5 B. ( E ), Flow cytometric analysis of R8H283 or H2A011 reactivity against each cell subset in the normal and tumor regions of the resected lung tissue. The analysis of UPN 7 is shown as a representative of three tested samples.

CAR T cells derived from R8H283 specifically recognized and killed NSCLC cells

CAR T cells derived from R8H283, but not those derived from a CD19 antibody (used as a control because they are specific for an irrelevant target), secreted IFN-γand IL-2, and exhibited cytotoxic activity when co-cultured with A549 lung cancer cells, but not when co-cultured with CD98hc-deficient A549 cells established using CRISPR-Cas9 (Fig. 4 A,B). R8H283 CAR T cells produced minimal amounts of cytokines upon co-culture with normal lung epithelial cells purified from normal regions of resected lung tissues of patients with NSCLC (Fig. 4 C–E).

CAR T cells derived from R8H283 specifically recognize and kill NSCLC cells. ( A ), Secretion of IFN-γ and IL-2 by R8H283 CAR T cells or CD19 CAR T cells (a control cell type targeting an irrelevant antigen) after co-culture with WT or CD98hc-deficient (CD98hc KO) A549 NSCLC cells. ( B ), 51 Cr release assay for measurement of specific lysis of WT or CD98hc KO A549 cells by R8H283 CAR T cells or CD19 CAR T cells. E/T, effector/target ratio. C, Experimental design for D and ( E . D ), Representative flow cytometry analysis of normal lung tissue. CD45 - CD31 - Ep-CAM + normal lung epithelial cells were purified and subjected to the assay. ( E ), Secretion of IFN-γ and IL-2 by R8H283 CAR T cells after co-culture with normal lung epithelial cells or A549 NSCLC cells. ( F ), Experimental design for G - I . IVIS, in vivo imaging system; i.v., intravenous. ( G ), Bioluminescence imaging of mice infused with either R8H283 or CD19 CAR T cells. ( n = 6 per group). Min, minimum. ( H ), Quantification of whole-body luminescence. Avg., average; p, photons; s, second; sr, steradian. ( I ), Survival curves of mice infused with either R8H283 or CD19 CAR T cells. The data are presented as means ± SEM. * P < 0.05 and ** P < 0.01 were calculated using two-tailed Student’s t- test ( H ) and the generalized Wilcoxon test ( I ).

In a lung cancer xenograft model established by intravenous injection of luciferase-expressing A549 cells into NOG mice 29 , infusion of R8H283 CAR T cells, but not CD19 CAR T cells, significantly decreased the tumor burden as determined by bioluminescence imaging (Fig. 4 F–H) and enhanced mouse survival (Fig. 4 I). No unexpected side effects were observed in mice injected with R8H283 CAR T cells. In a mouse that relapsed after R8H283 CAR T cell infusion, R8H283 reactivity to tumor cells was reduced compared to untreated tumor cells (Supplementary Fig. S7 ).

In this study, by thoroughly screening for NSCLC-specific mAbs from among approximately 10,000 mAbs raised against NSCLC cell lines, we found that the CD98hc-specific mAb H2A011 distinctly bound to most NSCLC cells but not to normal lung epithelial cells. Consistently, overexpression of CD98hc protein was previously demonstrated by immunohistochemistry in a subset of NSCLC samples 30 , 31 , 32 . CD98hc mRNA was not overexpressed in purified NSCLC cells compared with normal lung epithelial cells in two of three samples examined. In addition, according to the Cancer Genome Atlas, a gene expression database, CD98hc mRNA is also expressed in normal lung tissues at levels comparable with lung cancer tissues 33 . Furthermore, the analysis of publicly available single-cell RNAseq data showed that CD98hc mRNA expression did not differ between normal lung epithelial cells and NSCLC cells. These results showed that tumor-specific antigens that cannot be discovered by transcriptome analysis can be identified by thoroughly screening for tumor-specific mAbs among large numbers of mAbs raised against tumor cells, as we previously reported 23 , 24 , 34 , although the targets identified by the mAb discovery strategy are mostly conformational epitopes in proteins that are also expressed in normal tissues and on-target/off-tumor effects must be very carefully excluded.

The CD98 heterodimer is composed of CD98hc that is disulfide-linked with a light chain. The heavy chain binds to the cytoplasmic tails of integrin-β chains 35 , 36 , 37 and mediates adhesive signals that control cell spreading, survival, and growth 37 , 38 , 39 , 40 . The CD98 light chains (lcs) function in amino acid transport 41 , 42 and play important roles in the survival and growth of various cells 43 , 44 . Overexpression of CD98lc L-type amino acid transporter 1 (LAT1) in NSCLC 45 , 46 may enhance cell surface expression of CD98hc/lc heterodimers. The mechanisms of CD98hc protein overexpression on the surface of NSCLC cells should be clarified in future studies.

H2A011 CAR-transduced T cells failed to survive after 10 days of in vitro culture. While the loss of H2A011 CAR T cells could be caused by fratricide, ligand-dependent suboptimal CAR signaling could cause apoptosis of H2A011 CAR T cells as shown in a previous study 47 . In contrast, T cells transduced with the CAR derived from another anti-CD98hc mAb R8H283 could be expanded, although the in vitro expansion of R8H283 CAR T cells was not as good as that of control T cells. R8H283 reactivity in activated T cells may cause partial loss of R8H283 CAR T cells during in vitro culture.

R8H283, which has been shown to lack reactivity with CD98hc glycoforms expressed on normal hematopoietic cells and some normal tissues, reacted with NSCLC cells in a subset of patients. In the samples that we were able to analyze in pairs (tumor vs normal epithelial cells), R8H283 reacted with tumor cells but not with normal lung epithelial cells (UPN 7, 10, 11), although paired analysis of more samples should be performed in the future. In a previous report, we showed that R8H283 did not react with normal lymphocytes, monocytes, or non-hematopoietic cells such as intestinal epithelial cells or skin epidermal cells, although CD98hc protein is expressed on these cells 24 . We demonstrated that CAR T cells derived from R8H283 exerted a significant anti-tumor effect in an in vivo xenograft model. These results suggest that R8H283 CAR T cells have the potential to specifically target NSCLC cells without damaging normal cells in a subset of NSCLC patients, while the possibility of immune escape of tumor cells with low R8H283 reactivity should be carefully evaluated. Most of R8H283-reactive tumors in this study were squamous cell carcinomas, suggesting that R8H283-derived therapies will be useful in patients with squamous cell carcinoma, although a larger number of NSCLC samples should be analyzed in the future.

Although CD98hc protein expression on the surface of tumor cells was detected in most patients with NSCLC, reactivity to R8H283 was observed in only six of the 15 patients examined in this study. R8H283, which is expected to have a lower affinity for CD98hc than MEM108, may only react with NSCLC cells that express high levels of CD98hc. While the reactivity of R8H283 is certainly affected by alterations in the N-glycosylation of CD98hc, it remains unclear whether the NSCLC-specific reactivity of R8H283 is associated with alterations in the glycosylation of CD98hc in NSCLC cells.

A number of mAbs targeting CD98hc have been described in the context of cancer therapy 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , and some have been tested in clinical trials. Since CD98hc is expressed by several normal tissues, including normal lymphocytes, on-target off-tumor toxicity in normal tissues is always a concern when CD98hc is used as a therapeutic target. Although we showed that R8H283 reactivity was not detected in the normal human tissues that were available for testing 24 , it is difficult to completely exclude the possibility that under some conditions, the epitope recognized by R8H283 is formed in normal tissues expressing CD98hc. While the low levels of cytokine production by R8H283 CAR T cells co-cultured with normal lung epithelial cells is likely to reflect the spontaneous secretion from R8H283 cells, we could not completely exclude the possibility that R8H283 CAR T cells may be weakly reactive with normal lung epithelial cells. Since R8H283 does not react with mouse CD98hc, it is impossible to analyze the toxicity of R8H283 CAR T cells against normal cells in mouse xenograft models. Therefore, we must carefully examine the off-tumor reactivity of R8H283 before initiating a clinical study. In addition, it may be beneficial to develop a logic-gated CAR 56 , 57 , 58 that recognizes only cells expressing both the R8H283 antigen and another NSCLC-specific antigen, for example mesothelin.

Clinical samples

Lung tissue specimens from patients diagnosed with adenocarcinoma, squamous cell carcinoma, or pleomorphic carcinoma and who underwent surgical resection were used after written informed consent was obtained. This study conformed to the ethical guidelines outlined in the Declaration of Helsinki, and was approved by the institutional review boards of the Osaka University School of Medicine, Osaka Toneyama Medical Center, Osaka International Cancer Institute, Takarazuka City Hospital, Toyonaka Municipal Hospital, Suita Municipal Hospital, Minoh City Hospital, Kinki-Chuo Chest Medical Center, and Osaka Fukujuji Hospital.

The A549, H1792, H1975, H2228, and HCC827 cell lines were purchased from the American Type Culture Collection (ATCC). The SP2/0 mouse myeloma cell line was kindly gifted by I. Weissman (Stanford University). The Expi293 and Expi293 GnTI-deficient cell lines were purchased from Thermo Fisher Scientific. A549 cells expressing green fluorescent protein (GFP) and firefly luciferase (A549-GFP-luc) were established by retroviral transduction. Following gene transduction, GFP high cells were enriched by fluorescence-activated cell sorting (FACS) on a BD FACS Aria II (Becton Dickinson). CD98-deficient A549 cells were established using CRISPR-Cas9, as previously reported 24 .

Flow cytometry and cell sorting

To prepare single-cell suspensions from lung tissues, samples were dissociated using the Human Tumor Dissociation Kit (Miltenyi Biotech) and gentleMACS Octo Dissociator with Heaters (Miltenyi Biotech). After tissue dissociation, cell suspensions were filtered through a cell strainer (Corning) and red blood cells were lysed using ACK Lysing Buffer (Gibco). Cells were stained with the indicated mAbs after incubation with Human Serum AB (GeminiBio) and FcR Blocking Reagent Human (Miltenyi Biotech). The following antibodies were used: anti-human CD326 (Ep-CAM)-PE/Cyanine7 (9C4, BioLegend), anti-human CD45-APC (HI130, BioLegend), anti-human CD45-FITC (HI130, BioLegend), anti-human CD31-APC (WM-59, Invitrogen), anti-human CD3-FITC (SK7, BioLegend), anti-human CD19-APC/Cyanine7 (HIB19, BioLegend), and anti-human CD14-APC (M5E2, BioLegend), goat anti-mouse IgG, F(ab’)2 Fragment Specific-Alexa Fluor 647 (115–605-072, Jackson ImmunoResearch), Goat anti-mouse IgG-PE (405,307, BioLegend, Poly4053). H2A011 (mouse IgG1) and R8H283 (mouse IgG2a) were purified from hybridoma supernatants with Protein G Sepharose 4 Fast Flow (GE Healthcare) and used for staining at a concentration of 10 µg/ml and 50 µg/ml, respectively. Flow cytometry analysis and cell sorting were performed using a BD Canto II and Aria II (Becton Dickinson).

Phytohemagglutinin P (PHA)-activated T cells were generated by culturing human PBMC in the presence of 3ug/ml PHA (Sigma) for 72 h, stained with R8H283 or H2A011, then with goat anti-mouse IgG-PE, and analyzed on flow cytometry. Peripheral blood mononuclear cells were activated with anti-CD3 (OKT3, eBioscience) and anti-CD28 (CD28.2, eBioscience) mAbs and cultured in X-VIVO 15 (Lonza) supplemented with 5% human Sserum AB (GeminiBio) for 24 h, stained with biotinylated R8H283 or H2A011, then with streptavidin-PE (BioLegend), and analyzed on flow cytometry. R8H283 or H2A011 was biotinylated using biotin labeling kit (Dojindo).

RNA sequencing

NSCLC cells and unaffected lung epithelial cells were purified by FACS. Total RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific) and the RNeasy Mini Kit (QIAGEN). Full-length cDNA was generated using the SMART-Seq HT Kit (Takara Bio). Each library was prepared using a Nextera XT DNA Library Prep Kit (Illumina). Whole-transcriptome sequencing was performed on RNA samples using an Illumina HiSeq 3000 platform (Illumina) in 100-base single-end mode. Sequenced reads were mapped to human reference genome sequences (hg19) using TopHat v2.0.13 in combination with Bowtie2 ver. 2.2.3 and SAMtools ver. 0.1.19. The number of fragments per kilobase of exon per million mapped fragments was calculated using Cufflinks ver. 2.2.1. RNA sequencing data concerning this study have been deposited in the Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE226774 ). The data sets were analyzed using Ingenuity Pathway Analysis (Ingenuity Systems Inc).

Single-cell RNA sequencing

To confirm the expression of CD98hc (SLC3A2), publicly available single-cell RNA sequencing data was re-analyzed. We extracted the data of Laughney et al., which included the samples from eight lung NSCLC cells and four normal lungs from the human lung cell atlas dataset. We evaluated the gene expression changes in epithelial cells between the NSCLC tissues and normal lung tissues using the Wilcoxon rank-sum test, employing the Seurat FindMarkers function. We then checked the CD98hc data from the output. The P -value was corrected using the Bonferroni method for all genes expressed in the epithelial cells.

Generation of anti-NSCLC mAbs

Six- to eight-week-old BALB/cAJcl mice (CLEA Japan) were immunized by footpad injection with human NSCLC cell lines (A549, H1792, H1975, H2228, or HCC827). Lymphocytes from popliteal lymph nodes were fused with SP2/0 mouse myeloma cells in PEG solution (Roche Applied Science). To identify hybridoma clones producing mAbs that reacted with NSCLC cells, NSCLC cells were first incubated with hybridoma supernatants, then incubated with PE-conjugated anti-mouse IgG antibody and analyzed by flow cytometry. Hybridoma clones producing mAbs that reacted with NSCLC cells were selected and stocked for further analyses.

Expression cloning

Expression cloning was performed as previously reported 25 . A cDNA library was generated from A549 cells using the Superscript Choice System (Invitrogen) and linked with a BstXI adaptor. cDNA fragments ranging from 2.0 to 5.0 kb were selected on a CHROMA SPIN column (Takara Bio), purified by agarose gel electrophoresis, and then cloned into retrovirus vector pMX (a kind gift from T. Kitamura, Tokyo University). The A549 cDNA library was subjected to screening by transduction into Ba/F3 cells. Ba/F3 cells with which H2A011 reacted were enriched by FACS, then subjected to PCR cloning of the inserted cDNA.

Development of CAR T cells

cDNA of the variable region of H2A011 or R8H283 was obtained by 5’-RACE PCR with a Smarter RACE PCR Kit (Takara Bio), then sequenced. The isolated cDNAs of the κ light and heavy chain variable regions were fused to CD28 (Uniprot P10747 aa.114–220) and CD3ζ(Uniprot P20963 aa.52–164) cDNAs by overlapping PCR. Sequences of the leader peptides, linker, and variable regions of the κ light and the heavy chains of H2A011 are listed in Supplementary Table S2 . The sequences of the variable regions of the κ light and the heavy chains of R8H283 are shown in the patent (WO2017026497A1). The resultant H2A011 or R8H283 CAR constructs were inserted into pMSCV retroviral vectors. The CD19 CAR was constructed according to the reported sequences of the anti-CD19 mAb 59 , 60 . Then, 293 T cells were co-transfected with retroviral vector, gag-pol, and VSV-G envelope plasmids with Lipofectamine 2000 reagent (Thermo Fisher Scientific). Supernatants containing the retrovirus were collected 48 h and 72 h later. Activated T cells were infected with retrovirus carrying the H2A011 or R8H283 CAR. Briefly, peripheral blood mononuclear cells were activated with anti-CD3 (OKT3, eBioscience) and anti-CD28 (CD28.2, eBioscience) mAbs and cultured in X-VIVO 15 (Lonza) supplemented with 5% Human Serum AB (GeminiBio). The next day, recombinant human IL-2 (Shionogi Pharma) was added to the culture at a final concentration of 100 IU/ml. Cells were harvested 2 d after activation, then subjected to retroviral transduction with RetroNectin (Takara Bio). After transduction, the cells were cultured in the presence of 100 IU/ml IL-2 for 7 d. Dasatinib (1 µM) was added to the culture medium beginning 4 d after CAR transduction to prevent T-cell exhaustion, as previously described 61 . The transduction efficiency of each CAR was measured by staining cells with goat anti-mouse F(ab′) 2 -Alexa Fluor 647 mAb.

Cytokine release assays

R8H283 CAR T cells or mock-transduced (control) T cells were tested for reactivity in cytokine release assays. Cytokine concentrations were measured using an ELISA kit (IFN-γ and IL-2; R&D Systems). Effector cells and target cells (1.0 × 10 5 cells each) were co-cultured for 16 h. Co-culture was performed in technical-triplicate wells. Cytokine secretion was measured in culture supernatants diluted to fall within the linear range of the assay.

Cytotoxicity assay

The cytotoxic ability of CAR T cells was evaluated by 51 Cr release assay. Briefly, target cells were labeled for 90 min at 37ºC with 25 μCi of [ 51 Cr] sodium chromate (PerkinElmer). Labeled target cells (1.0 × 10 4 ) were incubated with effector cells for 4 h at the indicated effector/target ratios. 51 Cr release in harvested supernatants was counted with a gamma counter. Total and spontaneous 51 Cr release was determined by incubation of 1.0 × 10 4 labeled target cells in either 1% Triton X-100 or culture medium. The percentage of specific lysis was calculated as ([specific 51 Cr release − spontaneous 51 Cr release] / [total 51 Cr release − spontaneous 51 Cr release]) × 100.

Immunoblotting

Total cell lysate was prepared in lysis buffer [10 mM Tris–HCl pH 7.5, 150 mM NaCl, 1 mM EDTA, 0.1% NP-40, and protease inhibitor cocktail (Nacalai Tesque)]. Cell lysates from normal lung epithelial cells or NSCLC cells were run on a 4–12% NuPAGE gel system (Invitrogen) under reducing (0.7 M 2ME) or non-reducing conditions. To remove N-glycans attached to proteins, cell lysates were incubated at 37 °C for 30 min with 1,000 units of PNGase F PRIME (N-Zyme Scientifics), and then subjected to SDS-PAGE. Western blotting was carried out with anti-CD98 polyclonal Ab (pAb) (#15,193–1-AP, ProteinTech) and subsequently with HRP-conjugated donkey anti–rabbit IgG (#NA934V, GE Healthcare). Imaging of blots was performed using the LAS system (GE Healthcare).

In vivo xenograft mouse models

Female NOD/SCID/IL-2Rγcnull (NOG) mice aged 6–8 weeks (In-Vivo Science) were injected intravenously via the tail vein with 2.0 × 10 5 A549-luc/GFP tumor cells. Two days after tumor inoculation, the mice were intraperitoneally infused with VIVOGlo Luciferin (Promega, 150 mg/kg body weight), anesthetized with isoflurane, and imaged using an in vivo imaging system (IVIS) (PerkinElmer). The mice were then injected intravenously with CD19 or R8H283 CAR T cells (5.0 × 10 6 cells/mouse). Mice were reanalyzed with the IVIS every week. To minimize suffering and distress, mice were subjected to inhaled anesthesia (isoflurane) before cell injection. The health status of the mice was carefully examined three times per week by a veterinarian. Mice were euthanized when moribund or as recommended by a veterinarian. Investigators were not blinded.

Animal experiments

All mouse experiments in this study were approved by the administrative panel on Laboratory Animal Care at Osaka University (Ethical Approval ID 03–071 (for mAb production), 28–054 and 03–045 (for xenograft models)). Mice were euthanized by CO2 asphyxia. This study conforms to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health and is reported in accordance with ARRIVE guidelines.

Statistical analyses

Statistical analyses for significant differences between two groups were conducted using unpaired two-tailed Student’s t -test. The generalized Wilcoxon test was used to compare survival differences between the two groups. P < 0.05 was considered to indicate a significant difference. Statistical analyses were performed in GraphPad Prism 9.

Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Y. Kadota (Osaka Habikino Medical Center) and Y. Susaki (Ikeda City Hospital) for clinical samples. They also thank K. Terasaki for technical assistance, and I. Weissman (Stanford University) and T. Kitamura (Tokyo University) for providing materials.

This work was supported in part by the Japan Agency for Medical Research and Development (AMED) (23ama221318h0001 to N.H), the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP19K16799 and JP21K15487 to K.H., and JP19H04810 and JP20H03710 to N.H.), and by grants from the Yasuda Kinen Medical Foundation (to N.H.), the SENSHIN Medical Research Foundation (to N.H.), KAKETSUKEN (to N.H.), the Uehara Memorial Foundation (to N.H.), the Astellas Foundation for Research on Metabolic Disorders (to N.H.), and the Takeda Science Foundation (to N.H.).

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M.Y., K.H., and N.H. designed the experiments; M.Y., K.H., S.I., W.T., M. Matsubara, T.H., H.U., M.T., Y.O., M.S., H.M., Y.K., H.A. and D.O. performed the experiments; M.Y., W.T., M. Matsubara, H.U., M.T., T.H., T. Kimura, K. Kawagishi, S. Kawanaka, H. Yamato, Y. Takeuchi, E.O., T. Kanzaki, J.O., I.N, S.N., A. Kobayashi, T.I., T. Tokunaga, H. Yokouchi, Y.Y, J.U., M. Mori, K. Komuta, T. Tachi, H. Kuroda, N.K., H. Kishima, S.F., Y.N., T.S., K.M., S. Koyama, H.H., Y. Takeda, and Y.S. collected and analyzed clinical samples; M.Y., K.H., M. Matsubara, Y.S., D.O., and N.H. analyzed the data; M.Y., D.O., A. Kumanogoh, and N.H. wrote the manuscript; and all authors reviewed and approved the final version of the manuscript.

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Yaga, M., Hasegawa, K., Ikeda, S. et al. CD98 heavy chain protein is overexpressed in non-small cell lung cancer and is a potential target for CAR T-cell therapy. Sci Rep 14 , 17917 (2024). https://doi.org/10.1038/s41598-024-68779-9

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DOI : https://doi.org/10.1038/s41598-024-68779-9

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Is there a link between chronic obstructive pulmonary disease and lung adenocarcinoma a clinico-pathological and molecular study.

1. Introduction

2. materials and methods, 2.1. study design and population, 2.2. morphological analyses, 2.3. dna extraction, 2.4. next generation sequencing, 2.5. bioinformatics analysis.

Variants with variant allele frequency (VAF) < 0.05 (5%) were excluded;
Variants with coverage < 100X were excluded;
Only exonic and splicing variants were kept;
Synonymous SNVs were removed;
Polymorphisms were excluded, defined as variants having minor allele frequency (MAF) > 0.01 according to Exome Sequencing Project (ESP, https://bio.tools/esp , accessed on 14 April 2023) OR Exome Aggregation Consortium (ExAC, https://ngdc.cncb.ac.cn/databasecommons/database/id/3774 , accessed on 14 April 2023) OR 1000 Genomes Project ( http://www.internationalgenome.org , accessed on 7 August 2024) OR Genome Aggregation Database ( https://gnomad.broadinstitute.org/ , accessed on 1 June 2024);
We excluded possibly benign variants and variants with uncertain significance according to ClinVar (database update of 20221231, labels considered: ‘Benign’, ‘Benign/Likely benign’, ‘Likely benign’, ‘Uncertain significance’).

2.6. Statistical Analysis

3.1. study population, 3.2. distribution of mutations in copd, smokers and non smokers tumor samples, 3.3. analysis of matched pathological/healthy tissues, 4. discussion, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

Characteristics	COPD/Smoker LUAD N = 38	Non-COPD/Smoker LUAD N = 54	Non-COPD/Non-Smoker LUAD N = 18	p-Value	q-Value
Sex				0.001	0.015
males	30 (79%)	34 (63%)	5 (28%)
females	8 (21%)	20 (37%)	13 (72%)
Age	72 (68, 75)	69 (62, 73)	65 (59, 72)	0.11	0.3
GOLD stage				>0.9	>0.9
I	18 (47.5%)	0 (NA%)	0 (NA%)
II	18 (47.5%)	0 (NA%)	0 (NA%)
III	2 (5%)	0 (NA%)	0 (NA%)
Smoking history (pack years)	40 (27.5–50)	36 (22–50)	-	0.245	0.67
FEV1 (% of predict)	76 (64, 87)	93 (88, 113)	117 (104, 130)	<0.001	<0.001
FEV1/FV (% of predict)	67 (58, 70)	83 (78, 85)	82 (78, 86)	<0.001	<0.001
FVC (% of predict)	92 (74, 101)	92 (82, 105)	119 (108, 128)	<0.001	0.002
DLCO/VA (%)	70 (48, 89)	80 (65, 91)	84 (81, 99)	0.085	0.2
SUV	6 (2, 11)	6 (2, 12)	2 (1, 5)	0.061	0.2
WBC (n × 10 /L)	7.35 (5.78, 8.68)	6.92 (5.45, 9.30)	5.52 (4.70, 7.01)	0.012	0.072
RBC (n × 10 /L)	4.50 (4.18, 5.00)	4.42 (4.14, 4.77)	4.62 (4.21, 4.96)	0.7	0.8
HgB (g/dL)	13.70 (12.35, 14.65)	13.70 (12.50, 14.70)	13.80 (13.03, 14.80)	0.7	0.8
Neutrophils (n × 10 /L)	4.40 (3.54, 5.74)	4.02 (3.06, 6.02)	2.87 (2.81, 4.52)	0.046	0.2
Neutrophils (%)	64 (56, 69)	62 (56, 69)	56 (52, 66)	0.3	0.4
Lymphocytes (n × 10 /L)	1.77 (1.48, 2.12)	1.75 (1.55, 2.06)	1.62 (1.39, 1.93)	0.7	0.8
Lymphocytes (%)	24 (20, 32)	26 (20, 32)	31 (24, 33)	0.3	0.4
Monocytes (n × 10 /L)	0.60 (0.49, 0.70)	0.57 (0.42, 0.70)	0.49 (0.40, 0.58)	0.082	0.2
Monocytes (%)	8.55 (7.03, 10.05)	8.00 (7.10, 9.80)	9.30 (6.90, 10.50)	0.7	0.8
Eosinophils (n × 10 /L)	0.16 (0.06, 0.26)	0.08 (0.05, 0.16)	0.12 (0.06, 0.26)	0.079	0.2
Eosinophils (%)	2.05 (1.33, 3.63)	1.30 (0.80, 2.30)	2.20 (1.30, 3.80)	0.029	0.13
Basophils (n × 10 /L)	0.030 (0.020, 0.040)	0.020 (0.010, 0.030)	0.020 (0.020, 0.030)	0.13	0.3
Basophils (%)	0.40 (0.30, 0.60)	0.30 (0.20, 0.50)	0.40 (0.30, 0.60)	0.2	0.3
ESR (mm/h)	21 (12, 32)	20 (10, 28)	14 (9, 20)	0.090	0.2
CRP (mg/L)	2.0 (1.4, 6.9)	2.9 (1.3, 4.7)	0.8 (0.3, 2.9)	0.029	0.13
Clinical stage				0.2	0.3
IA	7 (18.5%)	17 (31.5%)	6 (33%)
IB	16 (42%)	11 (20.5%)	9 (50%)
IIA	4 (10.5%)	3 (6%)	0 (0%)
IIB	7 (18.5%)	10 (18%)	2 (11%)
IIIA	4 (10.5%)	10 (18%)	1 (6%)
IIIB	0 (0%)	3 (6%)	0 (0%)

Characteristic	COPD/Smoker LUAD N = 38	Non-COPD/Smoker LUAD N = 54	Non-COPD/Non-Smoker LUAD N = 18	p-Value	q-Value
Tumor cells (%)	70 (50, 80)	65 (50, 80)	70 (70, 80)	0.5	0.7
Prevalent pattern				0.3	0.4
Lepidic pattern	1 (2.5%)	8 (15%)	1 (5.6%)
Acinar pattern	28 (74%)	32 (59%)	14 (78%)
Papillary pattern	1 (2.5%)	4 (7.5%)	0 (0%)
Solid pattern	8 (21%)	10 (18.5%)	3 (17%)
Lepidic pattern (%)	0 (0, 5)	2 (0, 24)	18 (5, 30)	0.008	0.057
Acinar pattern (%)	60 (42, 85)	50 (20, 75)	60 (45, 84)	0.14	0.3
Papillary pattern (%)	0 (0, 0)	0 (0, 8)	0 (0, 8)	0.7	0.8
Micropapillary pattern (%)	0 (0, 2)	0 (0, 1)	0 (0, 4)	>0.9	>0.9
Solid pattern (%)	10 (0, 35)	0 (0, 32)	0 (0, 0)	0.2	0.3
WHO Grading
1	1 (2%)	5 (9%)	1 (5%)
2	17 (45%)	21 (39%)	14 (78%)
3	20 (53%)	28 (52%)	3 (17%)
MIB1 (%)	20 (10, 58)	40 (20, 70)	10 (9, 21)	0.001	0.011
Necrosis (%)				0.2	0.3
0	6 (16%)	12 (22%)	7 (39%)
≤10%	19 (50%)	25 (46%)	7 (39%)
11–30%	4 (10%)	7 (13%)	4 (22%)
>30%	9 (24%)	10 (19%)	0 (0%)
Inflammation (%)				0.2	0.3
0	0 (0%)	0 (0%)	1 (6%)
≤10%	13 (34%)	24 (44%)	4 (22%)
11–30%	18 (47.5%)	23 (43%)	11 (61%)
>30%	7 (18.5%)	7 (13%)	2 (11%)
Fibrosis (%)				0.2	0.3
0	3 (8%)	4 (7%)	0 (0%)
≤10%	16 (42%)	22 (41%)	3 (17%)
11–30%	12 (32%)	18 (33%)	8 (44%)
>30%	7 (18%)	10 (19%)	7 (39%)
Vascular invasion				0.5	0.7
No	18 (47%)	23 (43%)	9 (50%)
Yes	20 (53%)	31 (57%)	9 (50%)
Pleural invasion				0.6	0.8
No	20 (53%)	27 (50%)	11 (61%)
Yes	18 (47%)	27 (50%)	7 (39%)
Type of visceral pleura invasion				>0.9	>0.9
PL0	20 (53%)	27 (50%)	11 (61%)
PL1	15 (39%)	23 (43%)	6 (33%)
PL2	3 (8%)	4 (7%)	1 (6%)
Perineural invasion				0.5	0.7
No	34 (89%)	50 (93%)	18 (100%)
Yes	4 (11%)	4 (7%)	0 (0%)
Lymph node invasion				0.089	0.2
No	30 (79%)	38 (70%)	17 (94%)
Yes	8 (21%)	16 (30%)	1 (6%)
Type of lymph node invasion				0.14	0.3
0	30 (79%)	38 (70%)	17 (94%)
1	6 (16%)	7 (13%)	1 (6%)
2	2 (5%)	9 (17%)	0 (0%)

Gene	COPD/Smoker LUAD N = 38	Non-COPD/Smoker LUAD N = 54	Non-COPD/Non-Smoker LUAD N = 18
KRAS	21 (55%)	27 (50%)	3 (17%)
EGFR	9 (24%)	12 (22%)	10 (56%)
NTRK3	8 (21%)	14 (26%)	5 (28%)
TP53	10 (26%)	15 (28%)	4 (22%)
NTRK2	8 (21%)	10 (19%)	1 (6%)
PIK3CA	7 (18%)	9 (17%)	0 (0%)
STK11	9 (24%)	13 (24%)	1 (6%)
MET	5 (13%)	10 (19%)	0 (0%)
NOTCH1	6 (16%)	11 (20%)	2 (11%)
FBXW7	5 (13%)	5 (9%)	0 (0%)

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Lunardi, F.; Nardo, G.; Lazzarini, E.; Tzorakoleftheraki, S.-E.; Comacchio, G.M.; Fonzi, E.; Tebaldi, M.; Vedovelli, L.; Pezzuto, F.; Fortarezza, F.; et al. Is There a Link between Chronic Obstructive Pulmonary Disease and Lung Adenocarcinoma? A Clinico-Pathological and Molecular Study. J. Pers. Med. 2024 , 14 , 839. https://doi.org/10.3390/jpm14080839

Lunardi F, Nardo G, Lazzarini E, Tzorakoleftheraki S-E, Comacchio GM, Fonzi E, Tebaldi M, Vedovelli L, Pezzuto F, Fortarezza F, et al. Is There a Link between Chronic Obstructive Pulmonary Disease and Lung Adenocarcinoma? A Clinico-Pathological and Molecular Study. Journal of Personalized Medicine . 2024; 14(8):839. https://doi.org/10.3390/jpm14080839

Lunardi, Francesca, Giorgia Nardo, Elisabetta Lazzarini, Sofia-Eleni Tzorakoleftheraki, Giovanni Maria Comacchio, Eugenio Fonzi, Michela Tebaldi, Luca Vedovelli, Federica Pezzuto, Francesco Fortarezza, and et al. 2024. "Is There a Link between Chronic Obstructive Pulmonary Disease and Lung Adenocarcinoma? A Clinico-Pathological and Molecular Study" Journal of Personalized Medicine 14, no. 8: 839. https://doi.org/10.3390/jpm14080839

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Non-small cell lung cancer (NSCLC) is one of the most common causes of cancer deaths worldwide 1,2. Although immune checkpoint blockade therapy has largely improved the prognosis of NSCLC 3 , 4 ...
Is There a Link between Chronic Obstructive Pulmonary Disease and Lung
Chronic Obstructive Pulmonary Disease (COPD) and lung cancer are strictly related. To date, it is unknown if COPD-associated cancers are different from the tumors of non-COPD patients. The main goal of the study was to compare the morphological/molecular profiles of lung adenocarcinoma (LUAD) samples of COPD, non-COPD/smokers and non-COPD/non-smokers, and to investigate if a genetic ...
Introduction to cancer and treatment approaches
Keywords. 1.1. Introduction. Cancer is the uninhibited growth and development of abnormal cells in the body, and is one of the foremost reasons of deaths throughout the world ( Paul and Jindal, 2017 ). These abnormal cells are commonly designated as cancerous cells, tumorous cells, or malignant cells. In 2018, cancer accounted for an estimated ...

Lung Cancer Research Results and Study Updates

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Advancement in Lung Cancer Diagnosis: A Comprehensive Review of Deep Learning Approaches

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Lung cancer

Treatment and care

Stages of care

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The N 6 -methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma

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Deep Learning Techniques to Diagnose Lung Cancer

Simple Summary

1. Introduction

2. Lung Imaging Techniques

3. Deep Learning-Based Imaging Techniques

4. Lung Cancer Prediction Using Deep Learning

4.1. Imaging Pre-Processing Techniques and Evaluation

4.1.2. Performance Metrics

4.2. Datasets

4.3. Lung Image Segmentation

4.4. Lung Nodule Detection

4.5. Lung Nodule Classification

5. Challenges and Future Research Directions

6. Conclusions

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Hackensack Meridian CDI Lab Publishes Papers Pointing the Way Toward an Exhaled-Breath Test for Lung Cancer

CD98 heavy chain protein is overexpressed in non-small cell lung cancer and is a potential target for CAR T-cell therapy

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Targeting PD-L1 in non-small cell lung cancer using CAR T cells

Pre-clinical validation of a pan-cancer CAR-T cell immunotherapy targeting nfP2X7

Preclinical development of CD126 CAR-T cells with broad antitumor activity

H2A011 reacted with NSCLC cells but not with normal lung epithelial cells

H2A011 recognized CD98hc

Successful development of CAR T cells derived not from H2A011, but from another anti-CD98hc mAb, R8H283, that lacks reactivity with CD98hc glycoforms expressed on normal hematopoietic cells.

R8H283 reacted with NSCLC cells in a subset of patients

CAR T cells derived from R8H283 specifically recognized and killed NSCLC cells

Clinical samples

Flow cytometry and cell sorting

RNA sequencing

Single-cell RNA sequencing

Generation of anti-NSCLC mAbs

Expression cloning

Development of CAR T cells

Cytokine release assays

Cytotoxicity assay

Immunoblotting

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