Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer

Lung Cancer Research and Treatment: Global Perspectives and Strategic Calls to Action

BACKGROUND

Lung cancer remains a critical public health issue, presenting multifaceted challenges in prevention, diagnosis, and treatment. This article aims to review the current landscape of lung cancer research and management, delineate the persistent challenges, and outline pragmatic solutions.

MATERIALS AND METHODS

Global experts from academia, regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), the National Cancer Institute (NCI), professional societies, the pharmaceutical and biotech industries, and patient advocacy groups were gathered by the New York Lung Cancer Foundation to review the state of the art in lung cancer and to formulate calls to action.

RESULTS

Improving lung cancer management and research involves promoting tobacco cessation, identifying individuals at risk who could benefit from early detection programs, and addressing treatment-related toxicities. Efforts should focus on conducting well-designed trials to determine the optimal treatment sequence. Research into innovative biomarkers and therapies is crucial for more personalized treatment. Ensuring access to appropriate care for all patients, whether enrolled in clinical trials or not, must remain a priority.

CONCLUSIONS

Lung cancer is a major health burden worldwide, and its treatment has become increasingly complex over the past two decades. Improvement in lung cancer management and research requires unified messaging and global collaboration, expanded education, and greater access to screening, biomarker testing, treatment, as well as increased representativeness, participation, and diversity in clinical trials.

The clinical value of combined detection of seven lung cancer-related autoantibodies in assisting the diagnosis of non-small-cell lung cancer

Aim: Evaluate the clinical value of lung-cancer-related autoantibodies (CAGE, GAGE7, GBU4-5, MAGEA1, P53, PGP9.5, SOX2) in auxiliary diagnosis of non-small-cell lung cancer (NSCLC).Methods: We detected the concentrations of above 7 antibodies and lung cancer markers (CEA, NSE, CYFRE21-1) and drew area under the receiver characteristic curve of 316 patients.Results: The concentrations of CAGE, GBU4-5, MAGEA1, P53, PGP9.5 and SOX2 of significantly higher than other groups (p < 0.01). The sensitivity of different stages and pathological types of NSCLC consistent. Among them, the sensitivity of combined-detection in diagnosing adenocarcinoma and squamous cell carcinoma significantly better than CEA, NSE and CYFRA21-1.Conclusion: The combined detection has better efficacy in assisting the diagnosis of NSCLC and has certain clinical promotion and application value.

Real-world first round results from a charity lung cancer screening program in East Asia

Background

Screening with low-dose computed tomography (LDCT) has been proven to potentially reduce the rate of mortality of lung cancer. Lack of real-world data outside of protocolized trials has been cited as an impediment to its more widespread implementation, especially in Asia. This report aims to provide such real-world data.

Methods

A single round of LDCT was provided through a community-based charity program in Hong Kong, China to asymptomatic adults with a family history of lung cancer and/or smoking history. Anonymized data from this program were analyzed.

Results

LDCT was performed for 99 participants, including 98 (99%) who had one or more family members with history of lung cancer, and 70 (71%) who were never-smokers. After a single round of screening, a positive LDCT was noted in 47 participants (47%). A sister with a history of lung cancer (28% vs. 8%, P=0.01) and a multiplex family (MF) (47% vs. 23%, P=0.02) were factors associated with a positive LDCT. After a median period of 10 months (range, 5-16 months) following LDCT, lung cancer (all adenocarcinoma) was diagnosed as a direct consequence of positive LDCT findings in six participants (6%), of whom four had stage I disease and five received surgery with curative intent. In the 47 participants with a positive LDCT, having a sister with a history of lung cancer was associated with an increased risk of lung cancer (relative risk =5.23; 95% confidence interval: 1.09-25.21). Detected lesions categorized as Lung Imaging Reporting and Data System (Lung-RADS) 3 or above (odds ratio =12.08; 95% confidence interval: 1.27-114.64) or deemed by an experienced specialist to be suspicious (odds ratio =63.33; 95% confidence interval: 5.48-732.29) were significantly more likely to turn out to be a lung cancer.

Conclusions

This real-world data demonstrates that a single round of LDCT screening at a community level in East Asia can detect potentially curable lung cancer at a rate comparable to those reported by protocolized trials. When considering future LDCT screening programs in East Asia, a family history of lung cancer may be a key factor indicating a person for screening, and how features of a LDCT-detected lesion should trigger further intervention warrant further definition.

Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model

Background

Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer.

Methods

Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed.

Results

The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115.

Conclusion

LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Genome-Wide Analysis Identifies Nuclear Factor 1C as a Novel Transcription Factor and Potential Therapeutic Target in SCLC

INTRODUCTION

Recent insights regarding mechanisms mediating stemness, heterogeneity, and metastatic potential of lung cancers have yet to be fully translated to effective regimens for the treatment of these malignancies. This study sought to identify novel targets for lung cancer therapy.

METHODS

Transcriptomes and DNA methylomes of 14 SCLC and 10 NSCLC lines were compared with normal human small airway epithelial cells (SAECs) and induced pluripotent stem cell (iPSC) clones derived from SAEC. SCLC lines, lung iPSC (Lu-iPSC), and SAEC were further evaluated by DNase I hypersensitive site sequencing (DHS-seq). Changes in chromatin accessibility and depths of transcription factor (TF) footprints were quantified using Bivariate analysis of Genomic Footprint. Standard techniques were used to evaluate growth, tumorigenicity, and changes in transcriptomes and glucose metabolism of SCLC cells after NFIC knockdown and to evaluate NFIC expression in SCLC cells after exposure to BET inhibitors.

RESULTS

Considerable commonality of transcriptomes and DNA methylomes was observed between Lu-iPSC and SCLC; however, this analysis was uninformative regarding pathways unique to lung cancer. Linking results of DHS-seq to RNA sequencing enabled identification of networks not previously associated with SCLC. When combined with footprint depth, NFIC, a transcription factor not previously associated with SCLC, had the highest score of occupancy at open chromatin sites. Knockdown of NFIC impaired glucose metabolism, decreased stemness, and inhibited growth of SCLC cells in vitro and in vivo. ChIP-seq analysis identified numerous sites occupied by BRD4 in the NFIC promoter region. Knockdown of BRD4 or treatment with Bromodomain and extra-terminal domain (BET) inhibitors (BETis) markedly reduced NFIC expression in SCLC cells and SCLC PDX models. Approximately 8% of genes down-regulated by BETi treatment were repressed by NFIC knockdown in SCLC, whereas 34% of genes repressed after NFIC knockdown were also down-regulated in SCLC cells after BETi treatment.

CONCLUSIONS

NFIC is a key TF and possible mediator of transcriptional regulation by BET family proteins in SCLC. Our findings highlight the potential of genome-wide chromatin accessibility analysis for elucidating mechanisms of pulmonary carcinogenesis and identifying novel targets for lung cancer therapy.

The pros and cons of lung cancer screening

Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment. CLINICAL RELEVANCE STATEMENT: Different perspectives are provided on LCS based on the updated literature. KEY POINTS: Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality. This review summarizes current evidence related to LCS. Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.

Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery

The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a growing role of artificial intelligence (AI) in medicine. Algorithms have become part of clinical care enhancing image reconstruction, detecting cancer or predicting individual risk to support treatment decisions and patient management. The entry into clinical care is determined by technological feasibility, integration into effective workflows, and immediacy of benefits. At the same time, research is advancing the integration of imaging data and other modalities such as genomics, and the linking of observations made at large scale with the understanding of underlying biological processes. AI will have impact in imaging and precision medicine not only because of the successful application of techniques established in other domains, but primarily because of the effective joint development of new technology and corresponding advance of diagnosis and care.

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures

BACKGROUND

This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT).

METHODS

Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking.

RESULTS

A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC.

CONCLUSIONS

Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.