Research progress on predicting the growth of pulmonary nodules based on CT imaging_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XUE Wenhe ^1,2 , CHEN Nan ¹ ,  LIU Lunxu ¹

1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. West China School of Medicine, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

LIU Lunxu, Email: lunxu_liu@aliyun.com

Keywords：

Pulmonary nodules; CT imaging; growth prediction; radiomics; deep learning; personalized treatment

DOI：

10.7507/1007-4848.202503063

Video：

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[Abstract ]The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth (e.g., baseline morphological parameters, dynamic indicators, and clinical characteristics), traditional prediction models (e.g., exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.

1.	好的, 以下是删除序号后面的圆点后的参考文献列表:.
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7.	Yankelevitz DF, Yip R, Henschke CI. Impact of duration of diagnostic workup on prognosis for early lung cancer. J Thorac Oncol, 2023, 18(4): 527-537.
8.	MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology, 2017, 284(1): 228-243.
9.	Christensen J, Prosper AE, Wu CC, et al. ACR Lung-RADS v2022: assessment categories and management recommendations. Chest, 2024, 165(3): 738-753.
10.	崔舒蕾, 王建卫. 肺磨玻璃结节干预策略. 中华肿瘤防治杂志, 2024, 31(4): 233-240.Cui SL, Wang JW. Intervention strategies for pulmonary ground-glass nodules. Chin J Cancer Prev Treat, 2024, 31(4): 233-240.
11.	刘士远. 重视肺结节临床处理中的过度诊疗问题. 中华放射学杂志, 2021, 55(11): 1113-1116.Liu SY. Paying attention to excessive diagnosis and treatment in clinical dealings of pulmonary nodules. Chin J Radiol, 2021, 55(11): 1113-1116.
12.	Lee JH, Hwang EJ, Lim WH, et al. Determination of the optimum definition of growth evaluation for indeterminate pulmonary nodules detected in lung cancer screening. PLoS One, 2022, 17(9): e0274583.
13.	Qi LL, Wu BT, Tang W, et al. Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation. Eur Radiol, 2020, 30(2): 744-755.
14.	Zhang Z, Zhou L, Min X, et al. Long-term follow-up of persistent pulmonary subsolid nodules: natural course of pure, heterogeneous, and real part-solid ground-glass nodules. Thorac Cancer, 2023, 14(12): 1059-1070.
15.	周莹莹, 陈志军. 基于临床可视化参数建立肺磨玻璃结节生长预测模型. 中国医学科学院学报, 2024, 46(2): 169-175.Zhou YY, Chen ZJ. A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters. ACTA Academiae Medicine Science, 2024, 46(2): 169-175.
16.	Gao C, Li J, Wu L, et al. The natural growth of subsolid nodules predicted by quantitative initial CT features: a systematic review. Front Oncol, 2020, 10: 318.
17.	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(6): 605-616.
18.	Borghesi A, Coviello FL, Scrimieri A, et al. Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study. Radiol Med, 2023, 128(6): 734-743.
19.	汪琼, 姜阁阁, 沈晶, 等. 肺磨玻璃结节生长规律及风险因素的CT长期随访研究. 国际医学放射学杂志, 2023, 46(1): 24-30.Wang Q, Jiang GG, Shen J, et al. Long-term CT follow-up study on growth pattern and risk factors of pulmonary ground-glass nodules. Int J Med Radiol, 2023, 46(1): 24-30.
20.	Qiu T, Ru X, Yin K, et al. Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis. Jpn J Radiol, 2020, 38(8): 761-770.
21.	Borghesi A, Farina D, Michelini S, et al. Pulmonary adenocarcinomas presenting as ground-glass opacities on multidetector CT: three-dimensional computer-assisted analysis of growth pattern and doubling time. Diagn Interv Radiol, 2016, 22(6): 525-533.
22.	Heuvelmans MA, Vliegenthart R, de Koning HJ, et al. Quantification of growth patterns of screen-detected lung cancers: the NELSON study. Lung Cancer, 2017, 108: 48-54.
23.	Tang EK, Wu YJ, Chen CS, et al. Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time. Quant Imaging Med Surg, 2024, 14(6): 3983-3996.
24.	He Y, Xiong Z, Zhang J, et al. Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods. J Cancer Res Clin Oncol, 2023, 149(12): 9937-9946.
25.	Liao RQ, Li AW, Yan HH, et al. Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images. Front Oncol, 2022, 12: 1002953.
26.	Zhang R, Tian P, Qiu Z, et al. The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice. J Thorac Dis, 2020, 12(5): 2019-2030.
27.	Collins VP, Loeffler RK, Tivey H. Observations on growth rates of human tumors. Am J Roentgenol Radium Ther Nucl Med, 1956, 76(5): 988-1000.
28.	Lee K, Kim HR, Park SI, et al. Natural progression of ground-glass nodules after curative resection for non-small cell lung cancer. J Korean Med Sci, 2021, 36(43): e266.
29.	Bogot NR, Kazerooni EA, Kelly AM, et al. Interobserver and intraobserver variability in the assessment of pulmonary nodule size on CT using film and computer display methods. Acad Radiol, 2005, 12(8): 948-956.
30.	de Margerie-Mellon C, Ngo LH, Gill RR, et al. The growth rate of subsolid lung adenocarcinoma nodules at chest CT. Radiology, 2020, 297(1): 189-198.
31.	Detterbeck FC, Gibson CJ. Turning gray: the natural history of lung cancer over time. J Thorac Oncol, 2008, 3(7): 781-792.
32.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
33.	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
34.	Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics. Radiol Med, 2021, 126(10): 1296-1311.
35.	Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology, 2019, 290(3): 783-792.
36.	Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol, 2016, 11(12): 2120-2128.
37.	Gao C, Yan J, Luo Y, et al. The growth trend predictions in pulmonary ground glass nodules based on radiomic CT features. Front Oncol, 2020, 10: 580809.
38.	Ma ZJ, Ma ZX, Sun YL, et al. Prediction of subsolid pulmonary nodule growth rate using radiomics. BMC Med Imaging, 2023, 23(1): 177.
39.	Xue LM, Li Y, Zhang Y, et al. A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules. Eur Radiol, 2022, 32(4): 2672-2682.
40.	Jin H, Li Z, Tong R, et al. A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection. Med Phys, 2018, 45(5): 2097-2107.
41.	Kang G, Liu K, Hou B, et al. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One, 2017, 12(11): e0188290.
42.	Dou Q, Chen H, Yu L, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
43.	Rafael-Palou X, Aubanell A, Bonavita I, et al. Re-identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks. Med Image Anal, 2021, 67: 101823.
44.	Tang Y, Li M, Lin B, et al. Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules. Transl Lung Cancer Res, 2023, 12(12): 2494-2504.
45.	Tao G, Zhu L, Chen Q, et al. Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study. Transl Lung Cancer Res, 2022, 11(2): 250-262.
46.	Wang Y, Zhou C, Ying L, et al. Enhancing early lung cancer diagnosis: predicting lung nodule progression in follow-up low-dose CT scan with deep generative model. Cancers (Basel), 2024, 16(12).
47.	Fang J, Wang J, Li A, et al. Parameterized Gompertz-Guided morphological autoencoder for predicting pulmonary nodule growth. IEEE Trans Med Imaging, 2023, 42(12): 3602-3613.
48.	Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep, 2019 Mar 4, 9(1): 3358.

1. 好的, 以下是删除序号后面的圆点后的参考文献列表:.
2. 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.
3. Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. N Engl J Med, 2013, 369(3): 245-254.
4. Li N, Tan F, Chen W, et al. One-off low-dose CT for lung cancer screening in China: a multicentre, population-based, prospective cohort study. Lancet Respir Med, 2022, 10(4): 378-391.
5. 任益锋, 马琼, 蒋华, 等. 肺结节/早期肺癌预测模型的知识图谱与可视化分析. 中国胸心血管外科临床杂志, 2024, 32(1): 100-107.Ren YF, Ma Q, Jiang H, et al. Knowledge map and visualization analysis of pulmonary nodule/early-stage lung cancer prediction models. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 32(1): 100-107.
6. 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). 中华医学杂志, 2024, 104(34): 3175-3213.Oncology Society of Chinese Medical Association. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2024 edition). Natl Med J China, 2024, 104(34): 3175-3213.
7. Yankelevitz DF, Yip R, Henschke CI. Impact of duration of diagnostic workup on prognosis for early lung cancer. J Thorac Oncol, 2023, 18(4): 527-537.
8. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology, 2017, 284(1): 228-243.
9. Christensen J, Prosper AE, Wu CC, et al. ACR Lung-RADS v2022: assessment categories and management recommendations. Chest, 2024, 165(3): 738-753.
10. 崔舒蕾, 王建卫. 肺磨玻璃结节干预策略. 中华肿瘤防治杂志, 2024, 31(4): 233-240.Cui SL, Wang JW. Intervention strategies for pulmonary ground-glass nodules. Chin J Cancer Prev Treat, 2024, 31(4): 233-240.
11. 刘士远. 重视肺结节临床处理中的过度诊疗问题. 中华放射学杂志, 2021, 55(11): 1113-1116.Liu SY. Paying attention to excessive diagnosis and treatment in clinical dealings of pulmonary nodules. Chin J Radiol, 2021, 55(11): 1113-1116.
12. Lee JH, Hwang EJ, Lim WH, et al. Determination of the optimum definition of growth evaluation for indeterminate pulmonary nodules detected in lung cancer screening. PLoS One, 2022, 17(9): e0274583.
13. Qi LL, Wu BT, Tang W, et al. Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation. Eur Radiol, 2020, 30(2): 744-755.
14. Zhang Z, Zhou L, Min X, et al. Long-term follow-up of persistent pulmonary subsolid nodules: natural course of pure, heterogeneous, and real part-solid ground-glass nodules. Thorac Cancer, 2023, 14(12): 1059-1070.
15. 周莹莹, 陈志军. 基于临床可视化参数建立肺磨玻璃结节生长预测模型. 中国医学科学院学报, 2024, 46(2): 169-175.Zhou YY, Chen ZJ. A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters. ACTA Academiae Medicine Science, 2024, 46(2): 169-175.
16. Gao C, Li J, Wu L, et al. The natural growth of subsolid nodules predicted by quantitative initial CT features: a systematic review. Front Oncol, 2020, 10: 318.
17. 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(6): 605-616.
18. Borghesi A, Coviello FL, Scrimieri A, et al. Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study. Radiol Med, 2023, 128(6): 734-743.
19. 汪琼, 姜阁阁, 沈晶, 等. 肺磨玻璃结节生长规律及风险因素的CT长期随访研究. 国际医学放射学杂志, 2023, 46(1): 24-30.Wang Q, Jiang GG, Shen J, et al. Long-term CT follow-up study on growth pattern and risk factors of pulmonary ground-glass nodules. Int J Med Radiol, 2023, 46(1): 24-30.
20. Qiu T, Ru X, Yin K, et al. Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis. Jpn J Radiol, 2020, 38(8): 761-770.
21. Borghesi A, Farina D, Michelini S, et al. Pulmonary adenocarcinomas presenting as ground-glass opacities on multidetector CT: three-dimensional computer-assisted analysis of growth pattern and doubling time. Diagn Interv Radiol, 2016, 22(6): 525-533.
22. Heuvelmans MA, Vliegenthart R, de Koning HJ, et al. Quantification of growth patterns of screen-detected lung cancers: the NELSON study. Lung Cancer, 2017, 108: 48-54.
23. Tang EK, Wu YJ, Chen CS, et al. Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time. Quant Imaging Med Surg, 2024, 14(6): 3983-3996.
24. He Y, Xiong Z, Zhang J, et al. Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods. J Cancer Res Clin Oncol, 2023, 149(12): 9937-9946.
25. Liao RQ, Li AW, Yan HH, et al. Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images. Front Oncol, 2022, 12: 1002953.
26. Zhang R, Tian P, Qiu Z, et al. The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice. J Thorac Dis, 2020, 12(5): 2019-2030.
27. Collins VP, Loeffler RK, Tivey H. Observations on growth rates of human tumors. Am J Roentgenol Radium Ther Nucl Med, 1956, 76(5): 988-1000.
28. Lee K, Kim HR, Park SI, et al. Natural progression of ground-glass nodules after curative resection for non-small cell lung cancer. J Korean Med Sci, 2021, 36(43): e266.
29. Bogot NR, Kazerooni EA, Kelly AM, et al. Interobserver and intraobserver variability in the assessment of pulmonary nodule size on CT using film and computer display methods. Acad Radiol, 2005, 12(8): 948-956.
30. de Margerie-Mellon C, Ngo LH, Gill RR, et al. The growth rate of subsolid lung adenocarcinoma nodules at chest CT. Radiology, 2020, 297(1): 189-198.
31. Detterbeck FC, Gibson CJ. Turning gray: the natural history of lung cancer over time. J Thorac Oncol, 2008, 3(7): 781-792.
32. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
33. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
34. Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics. Radiol Med, 2021, 126(10): 1296-1311.
35. Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology, 2019, 290(3): 783-792.
36. Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol, 2016, 11(12): 2120-2128.
37. Gao C, Yan J, Luo Y, et al. The growth trend predictions in pulmonary ground glass nodules based on radiomic CT features. Front Oncol, 2020, 10: 580809.
38. Ma ZJ, Ma ZX, Sun YL, et al. Prediction of subsolid pulmonary nodule growth rate using radiomics. BMC Med Imaging, 2023, 23(1): 177.
39. Xue LM, Li Y, Zhang Y, et al. A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules. Eur Radiol, 2022, 32(4): 2672-2682.
40. Jin H, Li Z, Tong R, et al. A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection. Med Phys, 2018, 45(5): 2097-2107.
41. Kang G, Liu K, Hou B, et al. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One, 2017, 12(11): e0188290.
42. Dou Q, Chen H, Yu L, et al. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng, 2017, 64(7): 1558-1567.
43. Rafael-Palou X, Aubanell A, Bonavita I, et al. Re-identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks. Med Image Anal, 2021, 67: 101823.
44. Tang Y, Li M, Lin B, et al. Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules. Transl Lung Cancer Res, 2023, 12(12): 2494-2504.
45. Tao G, Zhu L, Chen Q, et al. Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study. Transl Lung Cancer Res, 2022, 11(2): 250-262.
46. Wang Y, Zhou C, Ying L, et al. Enhancing early lung cancer diagnosis: predicting lung nodule progression in follow-up low-dose CT scan with deep generative model. Cancers (Basel), 2024, 16(12).
47. Fang J, Wang J, Li A, et al. Parameterized Gompertz-Guided morphological autoencoder for predicting pulmonary nodule growth. IEEE Trans Med Imaging, 2023, 42(12): 3602-3613.
48. Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep, 2019 Mar 4, 9(1): 3358.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesResearch progress on predicting the growth of pulmonary nodules based on CT imaging

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