Interpretation of the NCCN clinical practice guidelines in oncology: Lung cancer screening (version 2.2024)_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

DONG Dong , ZHANG Yajie ,  LI Hecheng

Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China;

Corresponding author：

LI Hecheng, Email: lihecheng2000@hotmail.com

Keywords：

Lung cancer; screening; guideline; interpretation

DOI：

10.7507/1007-4848.202407037

Video：

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The National Comprehensive Cancer Network (NCCN) updates the "NCCN clinical practice guidelines in oncology: Lung cancer screening" annually, and the second edition of 2024 was released in October 2023. The 2024 edition of the guidelines builds on the 2023 edition with some updates on description and assessment of risk factors for lung cancer, evaluation and follow-up of lung nodules found during initial and subsequent screening, and low-dose CT screening protocols and imaging modalities. In this article, we will introduce the above updates and provide reference for lung cancer screening in China by combining the relevant guidelines and consensus in China.

Citation： DONG Dong, ZHANG Yajie, LI Hecheng. Interpretation of the NCCN clinical practice guidelines in oncology: Lung cancer screening (version 2.2024). Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(9): 1253-1257. doi: 10.7507/1007-4848.202407037 Copy

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 Cancer J Clin, 2024, 74(3): 229-263.
2.	郑荣寿, 陈茹, 韩冰峰, 等. 2022年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2024, 46(3): 221-231.Zheng RS, Chen R, Han BF, et al. Analysis of the prevalence of malignant tumors in China in 2022. Chin J Oncol, 2024, 46(3): 221-231.
3.	de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
4.	Rami-Porta R, Nishimura KK, Giroux DJ, et al. The International Association for the Study of Lung Cancer staging project: Proposals for revision of the TNM stage groups in the forthcoming (ninth) edition of the TNM classification for lung cancer. J Thorac Oncol, 2024, 19(7): 1007-1027.
5.	Shi JF, Wang L, Wu N, et al. Clinical characteristics and medical service utilization of lung cancer in China, 2005-2014: Overall design and results from a multicenter retrospective epidemiologic survey. Lung Cancer, 2019, 128.
6.	Zeng H, Chen W, Zheng R, et al. Changing cancer survival in China during 2003-15: A pooled analysis of 17 population-based cancer registries. Lancet Glob Health, 2018, 6(5): e555-e567.
7.	Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
8.	Potter AL, Rosenstein AL, Kiang MV, et al. Association of computed tomography screening with lung cancer stage shift and survival in the United States: Quasi-experimental study. BMJ, 2022, 376: e069008.
9.	Yang D, Liu Y, Bai C, et al. Epidemiology of lung cancer and lung cancer screening programs in China and the United States. Cancer Lett, 2020, 468: 82-87.
10.	Cao M, Li H, Sun D, et al. Cancer screening in China: The current status, challenges, and suggestions. Cancer Lett, 2021, 506: 120-127.
11.	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: 20-26.
12.	National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: Lung cancer screening (version 2.2024). URL: https://www.nccn.org. Accessed on 2024-07-01.
13.	中华医学会肿瘤学分会, 中华医学会肺癌临床诊疗指南(2024版), 2024年中华肺癌学术大会 2024年5月 17—19日.Chinese Society of Clinical Oncology, Chinese Medical Association. Clinicald Diagnosis and treatment guidelines for lung cancer (2024 Edition). Chinese Lung Cancer Academic Conference 2024, May 17-19, 2024.
14.	Landy R, Cheung LC, Young CD, et al. Absolute lung cancer risk increases among individuals with >15 quit-years: Analyses to inform the update of the American Cancer Society lung cancer screening guidelines. Cancer, 2024, 130(2): 201-215.
15.	Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000-2004. MMWR Morb Mortal Wkly Rep, 2008, 57(45): 1226-1228.
16.	Krist AH, Davidson KW, Mangione CM, et al. Screening for lung cancer: US preventive services task force recommendation statement. JAMA, 2021, 325(10): 962-970.
17.	Pasquinelli MM, Tammemägi MC, Kovitz KL, et al. Risk prediction model versus united states preventive services task force lung cancer screening eligibility criteria: Reducing race disparities. J Thorac Oncol, 2020, 15(11): 1738-1747.
18.	Tammemägi MC, Ruparel M, Tremblay A, et al. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): Interim analysis of a prospective cohort study. Lancet Oncol, 2022, 23(1): 138-148.
19.	Choi E, Ding VY, Luo SJ, et al. Risk model-based lung cancer screening and racial and ethnic disparities in the US. JAMA Oncol, 2023, 9(12): 1640-1648.
20.	Sands J, Tammemägi MC, Couraud S, et al. Lung screening benefits and challenges: A review of the data and outline for implementation. J Thorac Oncol, 2021, 16(1): 37-53.
21.	Ziegelmayer S, Graf M, Makowski M, et al. Cost-effectiveness of artificial intelligence support in computed tomography-based lung cancer screening. Cancers, 2022, 14(7): 1729.
22.	Adams SJ, Mondal P, Penz E, et al. Development and cost analysis of a lung nodule management strategy combining artificial intelligence and lung-RADS for baseline lung cancer screening. J Am Coll Radiol, 2021, 18(5): 741-751.
23.	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(6): 954-961.
24.	Hata A, Yanagawa M, Yoshida Y, et al. Combination of deep learning-based denoising and iterative reconstruction for ultra-low-dose CT of the chest: Image quality and lung-RADS evaluation. AJR Am J Roentgenol, 2020, 215(6): 1321-1328.
25.	Obuchowski NA, Bullen JA. Statistical considerations for testing an AI algorithm used for prescreening lung CT images. Contemp Clin Trials Commun, 2019, 16: 100434.
26.	Setio AAA, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal, 2017, 42: 1-13.
27.	Jiang B, Li N, Shi X, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology, 2022, 303(1): 202-212.
28.	Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI Database: A systematic review. Diagnostics (Basel), 2019, 9(1): 29.
29.	Cui X, Zheng S, Heuvelmans MA, et al. Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program. Eur J Radiol, 2022, 146: 110068.
30.	Venkadesh KV, Setio AAA, Schreuder A, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology, 2021, 300(2): 438-447.
31.	Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
32.	Li D, Mikela Vilmun B, Frederik Carlsen J, et al. The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI: A systematic review. Diagnostics, 2019, 9(4).
33.	Aberle DR, Berg CD, Black WC, et al. The national lung screening trial: Overview and study design. Radiology, 2011, 258(1): 243-253.
34.	Henschke CI, Yankelevitz DF, Naidich DP, et al. CT screening for lung cancer: Suspiciousness of nodules according to size on baseline scans. Radiology, 2004, 231(1): 164-168.
35.	Henschke CI, Yankelevitz DF, Miettinen OS. Computed tomographic screening for lung cancer: The relationship of disease stage to tumor size. Arch Intern Med, 2006, 166(3): 321-325.
36.	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(17): 1763-1771.
37.	Murrmann GB, van Vollenhoven FHM, Moodley L. Approach to a solid solitary pulmonary nodule in two different settings-"Common is common, rare is rare". J Thorac Dis, 2014, 6(3): 237-248.
38.	Christensen JA, Nathan MA, Mullan BP, et al. Characterization of the solitary pulmonary nodule: 18F-FDG PET versus nodule-enhancement CT. AJR Am J Roentgenol, 2006, 187(5): 1361-1367.
39.	Ashraf H, Dirksen A, Loft A, et al. Combined use of positron emission tomography and volume doubling time in lung cancer screening with low-dose CT scanning. Thorax, 2011, 66(4): 315-319.
40.	Ohno Y, Koyama H, Matsumoto K, et al. Differentiation of malignant and benign pulmonary nodules with quantitative first-pass 320-detector row perfusion CT versus FDG PET/CT. Radiology, 2011, 258(2): 599-609.
41.	Hein PA, Romano VC, Rogalla P, et al. Linear and volume measurements of pulmonary nodules at different CT dose levels—Intrascan and interscan analysis. Rofo, 2009, 181(1): 24-31.
42.	Demb J, Chu P, Yu S, et al. Analysis of computed tomography radiation doses used for lung cancer screening scans. JAMA Intern Med, 2019, 179(12): 1650-1657.
43.	Zhao A, Fopma S, Agrawal R. Demystifying the CT radiation dose sheet. Radiographics, 2022, 42(4): 1239-1250.

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 Cancer J Clin, 2024, 74(3): 229-263.
2. 郑荣寿, 陈茹, 韩冰峰, 等. 2022年中国恶性肿瘤流行情况分析. 中华肿瘤杂志, 2024, 46(3): 221-231.Zheng RS, Chen R, Han BF, et al. Analysis of the prevalence of malignant tumors in China in 2022. Chin J Oncol, 2024, 46(3): 221-231.
3. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
4. Rami-Porta R, Nishimura KK, Giroux DJ, et al. The International Association for the Study of Lung Cancer staging project: Proposals for revision of the TNM stage groups in the forthcoming (ninth) edition of the TNM classification for lung cancer. J Thorac Oncol, 2024, 19(7): 1007-1027.
5. Shi JF, Wang L, Wu N, et al. Clinical characteristics and medical service utilization of lung cancer in China, 2005-2014: Overall design and results from a multicenter retrospective epidemiologic survey. Lung Cancer, 2019, 128.
6. Zeng H, Chen W, Zheng R, et al. Changing cancer survival in China during 2003-15: A pooled analysis of 17 population-based cancer registries. Lancet Glob Health, 2018, 6(5): e555-e567.
7. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
8. Potter AL, Rosenstein AL, Kiang MV, et al. Association of computed tomography screening with lung cancer stage shift and survival in the United States: Quasi-experimental study. BMJ, 2022, 376: e069008.
9. Yang D, Liu Y, Bai C, et al. Epidemiology of lung cancer and lung cancer screening programs in China and the United States. Cancer Lett, 2020, 468: 82-87.
10. Cao M, Li H, Sun D, et al. Cancer screening in China: The current status, challenges, and suggestions. Cancer Lett, 2021, 506: 120-127.
11. 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: 20-26.
12. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: Lung cancer screening (version 2.2024). URL: https://www.nccn.org. Accessed on 2024-07-01.
13. 中华医学会肿瘤学分会, 中华医学会肺癌临床诊疗指南(2024版), 2024年中华肺癌学术大会 2024年5月 17—19日.Chinese Society of Clinical Oncology, Chinese Medical Association. Clinicald Diagnosis and treatment guidelines for lung cancer (2024 Edition). Chinese Lung Cancer Academic Conference 2024, May 17-19, 2024.
14. Landy R, Cheung LC, Young CD, et al. Absolute lung cancer risk increases among individuals with >15 quit-years: Analyses to inform the update of the American Cancer Society lung cancer screening guidelines. Cancer, 2024, 130(2): 201-215.
15. Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000-2004. MMWR Morb Mortal Wkly Rep, 2008, 57(45): 1226-1228.
16. Krist AH, Davidson KW, Mangione CM, et al. Screening for lung cancer: US preventive services task force recommendation statement. JAMA, 2021, 325(10): 962-970.
17. Pasquinelli MM, Tammemägi MC, Kovitz KL, et al. Risk prediction model versus united states preventive services task force lung cancer screening eligibility criteria: Reducing race disparities. J Thorac Oncol, 2020, 15(11): 1738-1747.
18. Tammemägi MC, Ruparel M, Tremblay A, et al. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): Interim analysis of a prospective cohort study. Lancet Oncol, 2022, 23(1): 138-148.
19. Choi E, Ding VY, Luo SJ, et al. Risk model-based lung cancer screening and racial and ethnic disparities in the US. JAMA Oncol, 2023, 9(12): 1640-1648.
20. Sands J, Tammemägi MC, Couraud S, et al. Lung screening benefits and challenges: A review of the data and outline for implementation. J Thorac Oncol, 2021, 16(1): 37-53.
21. Ziegelmayer S, Graf M, Makowski M, et al. Cost-effectiveness of artificial intelligence support in computed tomography-based lung cancer screening. Cancers, 2022, 14(7): 1729.
22. Adams SJ, Mondal P, Penz E, et al. Development and cost analysis of a lung nodule management strategy combining artificial intelligence and lung-RADS for baseline lung cancer screening. J Am Coll Radiol, 2021, 18(5): 741-751.
23. 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(6): 954-961.
24. Hata A, Yanagawa M, Yoshida Y, et al. Combination of deep learning-based denoising and iterative reconstruction for ultra-low-dose CT of the chest: Image quality and lung-RADS evaluation. AJR Am J Roentgenol, 2020, 215(6): 1321-1328.
25. Obuchowski NA, Bullen JA. Statistical considerations for testing an AI algorithm used for prescreening lung CT images. Contemp Clin Trials Commun, 2019, 16: 100434.
26. Setio AAA, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal, 2017, 42: 1-13.
27. Jiang B, Li N, Shi X, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology, 2022, 303(1): 202-212.
28. Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI Database: A systematic review. Diagnostics (Basel), 2019, 9(1): 29.
29. Cui X, Zheng S, Heuvelmans MA, et al. Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program. Eur J Radiol, 2022, 146: 110068.
30. Venkadesh KV, Setio AAA, Schreuder A, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology, 2021, 300(2): 438-447.
31. Baldwin DR, Gustafson J, Pickup L, et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax, 2020, 75(4): 306-312.
32. Li D, Mikela Vilmun B, Frederik Carlsen J, et al. The performance of deep learning algorithms on automatic pulmonary nodule detection and classification tested on different datasets that are not derived from LIDC-IDRI: A systematic review. Diagnostics, 2019, 9(4).
33. Aberle DR, Berg CD, Black WC, et al. The national lung screening trial: Overview and study design. Radiology, 2011, 258(1): 243-253.
34. Henschke CI, Yankelevitz DF, Naidich DP, et al. CT screening for lung cancer: Suspiciousness of nodules according to size on baseline scans. Radiology, 2004, 231(1): 164-168.
35. Henschke CI, Yankelevitz DF, Miettinen OS. Computed tomographic screening for lung cancer: The relationship of disease stage to tumor size. Arch Intern Med, 2006, 166(3): 321-325.
36. 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(17): 1763-1771.
37. Murrmann GB, van Vollenhoven FHM, Moodley L. Approach to a solid solitary pulmonary nodule in two different settings-"Common is common, rare is rare". J Thorac Dis, 2014, 6(3): 237-248.
38. Christensen JA, Nathan MA, Mullan BP, et al. Characterization of the solitary pulmonary nodule: 18F-FDG PET versus nodule-enhancement CT. AJR Am J Roentgenol, 2006, 187(5): 1361-1367.
39. Ashraf H, Dirksen A, Loft A, et al. Combined use of positron emission tomography and volume doubling time in lung cancer screening with low-dose CT scanning. Thorax, 2011, 66(4): 315-319.
40. Ohno Y, Koyama H, Matsumoto K, et al. Differentiation of malignant and benign pulmonary nodules with quantitative first-pass 320-detector row perfusion CT versus FDG PET/CT. Radiology, 2011, 258(2): 599-609.
41. Hein PA, Romano VC, Rogalla P, et al. Linear and volume measurements of pulmonary nodules at different CT dose levels—Intrascan and interscan analysis. Rofo, 2009, 181(1): 24-31.
42. Demb J, Chu P, Yu S, et al. Analysis of computed tomography radiation doses used for lung cancer screening scans. JAMA Intern Med, 2019, 179(12): 1650-1657.
43. Zhao A, Fopma S, Agrawal R. Demystifying the CT radiation dose sheet. Radiographics, 2022, 42(4): 1239-1250.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Interpretation of the NCCN clinical practice guidelines in oncology: Lung cancer screening (version 2.2024)

Abstract Full text Figures/Tables Video References Cited by

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