Early-stage lung cancer detection via thin-section low-dose CT reconstruction combined with AI in non-high risk populations: a large-scale real-world retrospective cohort study

Abstract

Background: Current lung cancer screening guidelines recommend annual low-dose computed tomography (LDCT) for high-risk individuals. However, the effectiveness of LDCT in non-high-risk individuals remains inadequately explored. With the incidence of lung cancer steadily increasing among non-high-risk individuals, this study aims to assess the risk of lung cancer in non-high-risk individuals and evaluate the potential of thin-section LDCT reconstruction combined with artificial intelligence (LDCT-TRAI) as a screening tool.

Methods: A real-world cohort study on lung cancer screening was conducted at the West China Hospital of Sichuan University from January 2010 to July 2021. Participants were screened using either LDCT-TRAI or traditional thick-section LDCT without AI (traditional LDCT). The AI system employed was the uAI-ChestCare software. Lung cancer diagnoses were confirmed through pathological examination.

Results: Among the 259 121 enrolled non-high-risk participants, 87 260 (33.7%) had positive screening results. Within 1 year, 728 (0.3%) participants were diagnosed with lung cancer, of whom 87.1% (634/728) were never-smokers, and 92.7% (675/728) presented with stage I disease. Compared with traditional LDCT, LDCT-TRAI demonstrated a higher lung cancer detection rate (0.3% vs. 0.2%, P < 0.001), particularly for stage I cancers (94.4% vs. 83.2%, P < 0.001), and was associated with improved survival outcomes (5-year overall survival rate: 95.4% vs. 81.3%, P < 0.0001).

Conclusion: These findings highlight the importance of expanding lung cancer screening to non-high-risk populations, especially never-smokers. LDCT-TRAI outperformed traditional LDCT in detecting early-stage cancers and improving survival outcomes, underscoring its potential as a more effective screening tool for early lung cancer detection in this population.

Keywords

lung cancer / non-high risk / low-dose computerized tomography / thin-section / artificial intelligence

Acknowledgements

This work was supported by Non-communicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0506102/2023ZD0506100), the National Natural Science Foundation of China (Grant No. 92159302), the Science and Technology Project of Sichuan (Grant No. 2022ZDZX0018), 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No. ZYGD22009), the Natural Science Foundation of Sichuan Province (Grant No. 2023NSFSC1458), 1·3·5 Project of State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University (Grant No. RHM24204), the Science and Technology Project of Sichuan (Grant No. 2020YFS0573), the Major research programs of the Natural Science Foundation of China (Grant No. 91859203), and Key R & D plan of Sichuan Provincial Department of science and technology (Grant No. 2021YFS0072). We thank all study participants for their cooperation. We also thank Wan Xiong, West China Medical Publishers, Xin Sun and Jing Tan, Clinical Epidemiology and Evidence-based Medicine Center, and She-Yu Li, Department of Endocrine Metabolism, West China Hospital, Sichuan University, for their valuable comments on the manuscript.

Author contributions

Guiyi Ji (Conceptualization, Data curation, Formal analysis, Methodology, Resources, Writing—original draft, Writing—review & editing), Wenxin Luo (Project administration, Supervision, Visualization, Writing—review & editing), Yuan Zhu (Data curation, Resources), Bojiang Chen (Conceptualization, Supervision, Writing—review & editing), Miye Wang (Data curation, Resources), Lili Jiang (Data curation, Supervision), Ming Yang (Data curation, Resources), Weiwei Song (Formal analysis, Methodology), Peiji Yao (Data curation, Resources), Tao Zheng (Conceptualization, Supervision), He Yu (Methodology, Supervision), Rui Zhang (Data curation, Resources, Software), Chengdi Wang (Data curation, Resources), Renxin Ding (Data curation, Methodology, Resources), Xuejun Zhuo (Data curation, Resources), Feng Chen (Resources, Supervision), Jinnan Li (Data curation, Resources), Xiaolong Tang (Data curation, Resources), Jinghong Xian (Data curation, Resources), Tingting Song (Supervision, Writing—review & editing), Jun Tang (Data curation, Resources), Min Feng (Formal analysis, Software), Jun Shao (Methodology, Supervision, Writing—review & editing), and Weimin Li (Conceptualization, Methodology, Project administration, Resources, Supervision, Validation, Writing—review & editing).

Supplementary data

Supplementary data are available at PCMEDI online.

Conflict of interest

None declared. In addition, as a co-Editor-in-Chief of Precision Clinical Medicine, the corresponding author Weimin Li was blinded from reviewing and making decisions on this manuscript.

References

[1] Bray F, Laversanne M, Sung H et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A CancerJ Clinicians 2024;74:22963. https://doi.org/10.3322/caac.21834.

[2] Rami-Porta R, Bolejack V, Crowley J et al. The IASLC Lung Cancer Staging Project:Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2015;10: 990-1003. https://doi.org/10.1097/JTO.0000000000000559.

[3] Aberle DR, Adams AM, Berg CD et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409. https://doi.org/10.1056/NEJMoa1102873.

[4] de Koning HJ, van der Aalst CM, de Jong PA et al. Reduced LungCancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med 2020;382:503-13. https://doi.org/10.1056/NEJMoa1911793.

[5] Henschke CI, Yankelevitz DF, Libby DM et al. Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med 2006;355:1763-71. https://doi.org/10.1056/NEJMoa060476.

[6] Yang W, Qian F, Teng J et al. Community-based lung cancer screening with low-dose CT in China: Results of the baseline screening. Lung Cancer 2018;117:20-6. https://doi.org/10.1016/j.lungcan.2018.01.003.

[7] Wille MM, Dirksen A, Ashraf H et al. Results of the Randomized Danish Lung Cancer Screening Trial with Focus on HighRisk Profiling. Am J Respir Crit Care Med 2016;193:542-51. https://doi.org/10.1164/rccm.201505-1040OC.

[8] Wood DE, Kazerooni EA, Aberle D et al. NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022. J Natl Compr Canc Netw 2022;20:754-64. https://doi.org/10.6004/jnccn.2022.0036.

[9] Krist AH, Davidson KW, Mangione CM et al. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 2021;325:962-70. https://doi.org/10.1001/jama.2021.1117.

[10] Chinese Expert Group on Early Diagnosis and Treatment of Lung Cancer, China Lung Oncology Group. China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version). Zhongguo Fei Ai Za Zhi 2023;26:1-9. https://doi.org/10.3779/j.issn.1009-3419.2023.102.10.

[11] Chang GC, Chiu CH, Yu CJ et al. Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study. The Lancet Respiratory Medicine 2024;12:141-52. https://doi.org/10.1016/S2213-2600(23)00338-7.

[12] Kang HR, Cho JY, Lee SH et al. Role of Low-Dose Computerized Tomography in Lung Cancer Screening among Never-Smokers. J Thorac Oncol 2019;14:436-44. https://doi.org/10.1016/j.jtho.2018. 11.002.

[13] Ardila D, Kiraly AP, Bharadwaj S et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019;25:954-61. https://doi.org/10.1038/s41591-019-0447-x.

[14] Cellina M, Cacioppa LM, Ce M et al. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers 2023;15:4344. https://doi.org/10.3390/cancers15174344.

[15] Adams SJ, Stone E, Baldwin DR et al. Lung cancer screening. The Lancet 2023;401:390-408. https://doi.org/10.1016/S0140-6736(22)01694-4.

[16] Mu J, Huang J, Ao M et al. Advances in diagnosis and prediction for aggression of pure solid T 1 lung cancer. Precis Clin Med 2023;6:pbad020. https://doi.org/10.1093/pcmedi/pbad020.

[17] Xu X, Wang C, Guo J et al. MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks. Med Image Anal 2020;65:101772. https://doi.org/10.1016/j.media. 2020.101772.

[18] Wang C, Ma J, Zhang S et al. Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases. NPJ Digit Med 2022;5:124. https://doi.org/10.1038/s41746-022-00648-z.

[19] Zhang K, Liu X, Shen J et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 2020;182:1360. https://doi.org/10.1016/j.cell.2020.08.029.

[20] Wang G, Liu X, Shen J et al. A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images. Nat Biomed Eng 2021;5:509-21. https://doi.org/10.1038/s41551-021-00704-1.

[21] Sun S, Schiller JH, Gazdar AF. Lung cancer in never smokers-a different disease. Nat Rev Cancer 2007;7:778-90. https://doi.org/10.1038/nrc2190.

[22] He Y, Xiong Z, Tian D et al. Natural progression of persistent pure ground-glass nodules 10 mm or smaller: long-term observation and risk factor assessment. Jpn J Radiol 2023;41:605-16. https://doi.org/10.1007/s11604-022-01382-y.

[23] Li C, Jin Y, Deng Q et al. Development and validation of a nomogram based on CT texture analysis for discriminating minimally invasive adenocarcinoma from glandular precursor lesions in sub-centimeter pulmonary ground glass nodules. Oncol Lett 2024;27:26. https://doi.org/10.3892/ol.2023.14159.

[24] He Y, Xiong Z, Zhang J et al. Growth assessment of pure groundglass nodules on CT: comparison of density and size measurement methods. J Cancer Res Clin Oncol 2023;149:9937-46. https://doi.org/10.1007/s00432-023-04918-5.

[25] Gould MK, Donington J, Lynch WR et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guide- lines. Chest 2013;143:e93S-e120S. https://doi.org/10.1378/chest. 12-2351.

[26] Fritz A, Percy C, Jack A et al.Eds., International classification of diseases for oncology. 3rd. Geneva: World Health Organization; 2000. https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology. (2 December 2024, date last accessed).

[27] Amin MB, Edge SB, Greene FL et al.Eds., AJCC Cancer staging manual. 8th ed. New York: Springer; 2017. https://link.springer.com/book/9783319406176.(2 December 2024, date last accessed).

[28] Wang F, Tan F, Shen S et al. Risk-stratified approach for neverand ever-smokers in lung cancer Screening: A prospective cohort study in China. Am J Respir Crit Care Med 2023;207:77-88. https://doi.org/10.1164/rccm.202204-0727OC.

[29] Wang C, Shao J, Song L et al. Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases. Chin MedJ (Engl) 2023;136:1937-48. https://doi.org/10.1097/CM9.0000000000002729.

[30] Toh CK, Ong WS, Lim WT et al. A decade of never-smokers among lung cancer patients-increasing trend and improved survival. Clin Lung Cancer 2018;19:e539-50. https://doi.org/10.1016/j.cllc. 2 018.03.013.

[31] Cho J, Choi SM, Lee J et al. Proportion and clinical features of never-smokers with non-small cell lung cancer. Chin J Cancer 2017;36:20. https://doi.org/10.1186/s40880-017-0187-6.

[32] Pelosof L, Ahn C, Gao A et al. Proportion of never-smoker nonsmall cell lung cancer patients at three diverse institutions. JNCI J Natl Cancer Inst 2017;109:djw295. https://doi.org/10.1093/jnci/d jw295.

[33] LoPiccolo J, Gusev A, Christiani DC et al. Lung cancer in patients who have never smoked-an emerging disease. Nat Rev Clin Oncol 2024;21:121-46. https://doi.org/10.1038/s41571-023-0 0844-0.

[34] Kathuria H, Detterbeck FC, Fathi JT et al. Stakeholder Research Priorities for Smoking Cessation Interventions within Lung Can-cer Screening Programs. An Official American Thoracic Society Research Statement. Am J Respir Crit Care Med 2017;196:1202-12. https://doi.org/10.1164/rccm.201709-1858ST.

[35] Borondy Kitts AK, McKee AB, Regis SM et al. Smoking cessation results in a clinical lung cancer screening program. J Thorac Dis 2016;8:S481-7. https://doi.org/10.21037/jtd.2016.03.11.

[36] Hill W, Lim EL, Weeden CE et al. Lung adenocarcinoma promotion by air pollutants. Nature 2023;616:159-67. https://doi.org/10.1038/s41586-023-05874-3.

[37] Luo W, Tian P, Wang Y et al. Characteristics of genomic alterations of lung adenocarcinoma in young never-smokers. Intl J Cancer 2018;143:1696-705. https://doi.org/10.1002/ijc.31542.

[38] Li Y, Xiao X, Li J et al. Lung cancer in ever- and never-smokers: Findings from multi-population GWAS studies. Cancer Epidemiol Biomarkers Prev 2024;33:389-99. https://doi.org/10.1158/1055-9 965.EPI-23-0613.

[39] Cheng ES, Weber M, Steinberg J et al. Lung cancer risk in neversmokers: An overview of environmental and genetic factors. Chin J Cancer Res 2021;33:548-62. https://doi.org/10.21147/j.issn. 1000-9604.2021.05.02.

[40] Fidler-Benaoudia MM, Torre LA, Bray F et al. Lung cancer incidence in young women vs. young men: A systematic analysis in 40 countries. Intl J Cancer 2020;147:811-9. https://doi.org/10.100 2/ijc. 32809.

[41] Jemal A, Miller KD, Ma J et al. Higher lung cancer incidence in young women than young men in the United States. N Engl J Med 2018;378:1999-2009. https://doi.org/10.1056/NEJMoa1715 907.

[42] Li C, Liang H, Zhong N et al. Optimal starting age for lung cancer screening with low-dose computed tomography: A population level analysis. J Thorac Oncol 2019;14:e82-4. https://doi.org/10.1 016/j.jtho.2018.12.008.

[43] Nasrullah N, Sang J, Alam MS et al. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies. Sensors 2019;19:3722. https://doi.org/10.339 0/s19173722.