Advances in radiomics for early diagnosis and precision treatment of lung cancer_Journal of Biomedical Engineering

Authors：

LI Jiayi ^1,2,3,4 , LUO Wenxin ^1,2,3,4,5 , WANG Zhoufeng ^2,3,4 ,  LI Weimin ^1,2,3,4,5

1. Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
4. State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
5. Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

Keywords：

Lung cancer; Radiomics; Artificial intelligence; Personalized treatment

DOI：

10.7507/1001-5515.202405059

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of artificial intelligence in advancing the clinical application of radiomics, alongside future research directions.

1.	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.
2.	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 Med J (Engl), 2023, 136(16): 1937-1948.
3.	Jeon D S, Kim H C, Kim S H, et al. Five-year overall survival and prognostic factors in patients with lung cancer: results from the Korean Association of Lung Cancer Registry (KALC-R) 2015. Cancer Res Treat, 2023, 55(1): 103-111.
4.	Gillies R J, Kinahan P E, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
5.	Gillies R J, Schabath M B. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomarkers Prev, 2020, 29(12): 2556-2567.
6.	Xiushan Z, Bo H, et al. Diagnostic accuracy of deep learning and radiomics in lung cancer staging: a systematic review and meta-analysis. Front Public Health, 2022, 10: 938113.
7.	Mikhael P G, Wohlwend J, Yala A, et al. Sybil: A validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol, 2023, 41(12): 2191-2200.
8.	Saad M B, Hong L, Aminu M, et al. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study. Lancet Digit Health, 2023, 5(7): e404-e420.
9.	Zhou Y, Xu X, Song L, et al. The application of artificial intelligence and radiomics in lung cancer. Precis Clin Med, 2020, 3(3): 214-227.
10.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
11.	El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit, 2009, 42(6): 1162-1171.
12.	Haralick R M, Shanmugam K, Dinstein I H. Textural features for image classification. IEEE Trans Syst Man Cybern, 1973(6): 610-621.
13.	Cook G J, Siddique M, Taylor B P, et al. Radiomics in PET: principles and applications. Clin Transl Imaging, 2014, 2: 269-276.
14.	Zavala-Mondragon L A, Rongen P, Bescos J O, et al. Noise reduction in CT using learned wavelet-frame shrinkage networks. IEEE Trans Med Imaging, 2022, 41(8): 2048-2066.
15.	Liu A, Wang Z, Yang Y, et al. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond), 2020, 40(1): 16-24.
16.	Gao M, Jiang H, Hu Y, et al. Suppressing label noise in medical image classification using mixup attention and self-supervised learning. Phys Med Biol, 2024, 69(10): 105026.
17.	Zheng S, Ye X, Tan J, et al. Dual-attention deep fusion network for multi-modal medical image segmentation// Fourteenth International Conference on Graphics and Image Processing (ICGIP). Nanjing: SPIE, 2023: 127051R.
18.	Bi W L, Hosny A, Schabath M B, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin, 2019, 69(2): 127-157.
19.	Aerts H J W L, Velazquez E R, Leijenaar R T H, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4644.
20.	Chen M, Copley S J, Viola P, et al. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol, 2023, 93: 97-113.
21.	Aberle D R, Adams A M, Berg C D, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
22.	Becker N, Motsch E, Trotter A, et al. Lung cancer mortality reduction by LDCT screening-Results from the randomized German LUSI trial. Int J Cancer, 2020, 146(6): 1503-1513.
23.	De Koning H J, Van der Aalst C M, De Jong P A, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
24.	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.
25.	Tunali I, Gillies R J, Schabath M B. Application of radiomics and AI for lung cancer precision medicine. Cold Spring Harb Perspect Med, 2021, 11(8): a039537.
26.	Shao J, Wang G, Yi L, et al. Deep learning empowers lung cancer screening based on mobile low-dose computed tomography in resource-constrained sites. Front Biosci (Landmark Ed), 2022, 27(7): 212.
27.	Wu J, Li R, Zhang H, et al. Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study. Thorac Cancer, 2024, 15(19): 1522-1532.
28.	Bach P B, Mirkin J N, Oliver T K, et al. Benefits and harms of CT screening for lung cancer: a systematic review. JAMA, 2012, 307(22): 2418-2429.
29.	Zhu F, Yang C, Zou J, et al. The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis. Acta Radiol, 2023, 64(12): 3074-3084.
30.	Lin R, Zheng Y, Lv F, et al. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules. Med Phys, 2023, 50(5): 2835-2843.
31.	Warkentin M T, Al-Sawaihey H, Lam S, et al. Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches. Thorax, 2024, 79(4): 307-315.
32.	Deng H, Zhang J, Li J, et al. Clinical features and radiological characteristics of pulmonary cryptococcosis. J Int Med Res, 2018, 46(7): 2687-2695.
33.	Qu Y, Liu G, Ghimire P, et al. Primary pulmonary cryptococcosis: evaluation of CT characteristics in 26 immunocompetent Chinese patients. Acta Radiol, 2012, 53(6): 668-674.
34.	Hu B, Xia W, Piao S, et al. A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test. Transl Lung Cancer Res, 2023, 12(8): 1790-801.
35.	Li S, Zhang G, Yin Y, et al. One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed. Comput Med Imaging Graph, 2021, 94: 102009.
36.	Larue R T H M, van Timmeren J E, de Jong E E C, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol, 2017, 56(11): 1544-1553.
37.	Garau N, Paganelli C, Summers P, et al. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis. Med Phys, 2020, 47(9): 4125-4136.
38.	Grosu H B, Manzanera A, Shivakumar S, et al. Survival disparities following surgery among patients with different histological types of non-small cell lung cancer. Lung Cancer, 2020, 140: 55-58.
39.	Pei G, Wang D, Sun K, et al. Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study. Front Oncol, 2023, 13: 1224455.
40.	Choi W, Liu C J, Alam S R, et al. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma. Comput Struct Biotechnol J, 2023, 21: 5601-5608.
41.	Yotsukura M, Asamura H, Motoi N, et al. Long-term prognosis of patients with resected adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung. J Thorac Oncol, 2021, 16(8): 1312-1320.
42.	Ito M, Miyata Y, Kushitani K, et al. Pathological high malignant grade is higher risk of recurrence in pN0M0 invasive lung adenocarcinoma, even with small invasive size. Thorac Cancer, 2021, 12(23): 3141-3149.
43.	Wu F, Tian S P, Jin X, et al. CT and histopathologic characteristics of lung adenocarcinoma with pure ground-glass nodules 10 mm or less in diameter. Eur Radiol, 2017, 27(10): 4037-4043.
44.	Kodama K, Higashiyama M, Yokouchi H, et al. Natural history of pure ground-glass opacity after long-term follow-up of more than 2 years. Ann Thorac Surg, 2002, 73(2): 386-393.
45.	Eguchi T, Kondo R, Kawakami S, et al. Computed tomography attenuation predicts the growth of pure ground-glass nodules. Lung Cancer, 2014, 84(3): 242-247.
46.	Yang Y, Li K, Sun D, et al. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as pure ground-glass nodules: differentiation using enhanced dual-source dual-energy CT. AJR Am J Roentgenol, 2019, 213(3): 114-122.
47.	杨延涛, 王维, 杨逸辰, 等. CT定量和定性特征联合预测肺磨玻璃结节浸润程度555例的回顾性队列研究. 中国胸心血管外科临床杂志, 2024, 31(1): 51-58.
48.	Feng H, Shi G, Xu Q, et al. Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas. Insights Imaging, 2023, 14(1): 24.
49.	Pan Z, Hu G, Zhu Z, et al. Predicting invasiveness of lung adenocarcinoma at chest CT with deep learning ternary classification models. Radiology, 2024, 311(1): e232057.
50.	Han Y B, Kim H, Mino-Kenudson M, et al. Tumor spread through air spaces (STAS): prognostic significance of grading in non-small cell lung cancer. Mod Pathol, 2021, 34(3): 549-561.
51.	Feng Y, Ding H, Huang X, et al. Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma. NPJ Precis Oncol, 2024, 8(1): 173.
52.	Xu J, Liu L, Ji Y, et al. Enhanced CT-based intratumoral and peritumoral radiomics nomograms predict high-grade patterns of invasive lung adenocarcinoma. Acad Radiol, 2025, 32(1): 482-492.
53.	Altorki N, Wang X, Kozono D, et al. Lobar or sublobar resection for peripheral stage IA non-small-cell lung cancer. N Engl J Med, 2023, 388(6): 489-498.
54.	Mahvi D A, Liu R, Grinstaff M W, et al. Local cancer recurrence: the realities, challenges, and opportunities for new therapies. CA Cancer J Clin, 2018, 68(6): 488-505.
55.	Chen K, Yang A, Carbone D P, et al. Spatiotemporal genomic analysis reveals distinct molecular features in recurrent stage I non-small cell lung cancers. Cell Rep, 2022, 40(2): 111047.
56.	Khorrami M, Bera K, Leo P, et al. Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer, 2020, 142: 90-97.
57.	Shimada Y, Kudo Y, Maehara S, et al. Radiomics with artificial intelligence for the prediction of early recurrence in patients with clinical stage IA lung cancer. Ann Surg Oncol, 2022, 29(13): 8185-8193.
58.	Mattonen S A, Davidzon G A, Bakr S, et al. [18F] FDG positron emission tomography (PET) tumor and penumbra imaging features predict recurrence in non–small cell lung cancer. Tomography, 2019, 5(1): 145-153.
59.	Bove S, Fanizzi A, Fadda F, et al. A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region. PLoS One, 2023, 18(5): e0285188.
60.	Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable Non-Small Cell Lung Cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health, 2020, 2(3): 116-128.
61.	Kratz J R, He J, Van Den Eeden S K, et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet, 2012, 379(9818): 823-832.
62.	Zhu C Q, Ding K, Strumpf D, et al. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol, 2010, 28(29): 4417-4424.
63.	Borgli H, Thambawita V, Smedsrud P H, et al. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data, 2020, 7(1): 283.
64.	Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging, 2023, 14(1): 75.
65.	Price W, Nicholson I. Black-box medicine. Harv JL & Tech, 2014, 28(2): 419-461.

1. 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.
2. 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 Med J (Engl), 2023, 136(16): 1937-1948.
3. Jeon D S, Kim H C, Kim S H, et al. Five-year overall survival and prognostic factors in patients with lung cancer: results from the Korean Association of Lung Cancer Registry (KALC-R) 2015. Cancer Res Treat, 2023, 55(1): 103-111.
4. Gillies R J, Kinahan P E, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
5. Gillies R J, Schabath M B. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomarkers Prev, 2020, 29(12): 2556-2567.
6. Xiushan Z, Bo H, et al. Diagnostic accuracy of deep learning and radiomics in lung cancer staging: a systematic review and meta-analysis. Front Public Health, 2022, 10: 938113.
7. Mikhael P G, Wohlwend J, Yala A, et al. Sybil: A validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol, 2023, 41(12): 2191-2200.
8. Saad M B, Hong L, Aminu M, et al. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study. Lancet Digit Health, 2023, 5(7): e404-e420.
9. Zhou Y, Xu X, Song L, et al. The application of artificial intelligence and radiomics in lung cancer. Precis Clin Med, 2020, 3(3): 214-227.
10. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
11. El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit, 2009, 42(6): 1162-1171.
12. Haralick R M, Shanmugam K, Dinstein I H. Textural features for image classification. IEEE Trans Syst Man Cybern, 1973(6): 610-621.
13. Cook G J, Siddique M, Taylor B P, et al. Radiomics in PET: principles and applications. Clin Transl Imaging, 2014, 2: 269-276.
14. Zavala-Mondragon L A, Rongen P, Bescos J O, et al. Noise reduction in CT using learned wavelet-frame shrinkage networks. IEEE Trans Med Imaging, 2022, 41(8): 2048-2066.
15. Liu A, Wang Z, Yang Y, et al. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond), 2020, 40(1): 16-24.
16. Gao M, Jiang H, Hu Y, et al. Suppressing label noise in medical image classification using mixup attention and self-supervised learning. Phys Med Biol, 2024, 69(10): 105026.
17. Zheng S, Ye X, Tan J, et al. Dual-attention deep fusion network for multi-modal medical image segmentation// Fourteenth International Conference on Graphics and Image Processing (ICGIP). Nanjing: SPIE, 2023: 127051R.
18. Bi W L, Hosny A, Schabath M B, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin, 2019, 69(2): 127-157.
19. Aerts H J W L, Velazquez E R, Leijenaar R T H, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4644.
20. Chen M, Copley S J, Viola P, et al. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol, 2023, 93: 97-113.
21. Aberle D R, Adams A M, Berg C D, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
22. Becker N, Motsch E, Trotter A, et al. Lung cancer mortality reduction by LDCT screening-Results from the randomized German LUSI trial. Int J Cancer, 2020, 146(6): 1503-1513.
23. De Koning H J, Van der Aalst C M, De Jong P A, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
24. 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.
25. Tunali I, Gillies R J, Schabath M B. Application of radiomics and AI for lung cancer precision medicine. Cold Spring Harb Perspect Med, 2021, 11(8): a039537.
26. Shao J, Wang G, Yi L, et al. Deep learning empowers lung cancer screening based on mobile low-dose computed tomography in resource-constrained sites. Front Biosci (Landmark Ed), 2022, 27(7): 212.
27. Wu J, Li R, Zhang H, et al. Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study. Thorac Cancer, 2024, 15(19): 1522-1532.
28. Bach P B, Mirkin J N, Oliver T K, et al. Benefits and harms of CT screening for lung cancer: a systematic review. JAMA, 2012, 307(22): 2418-2429.
29. Zhu F, Yang C, Zou J, et al. The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis. Acta Radiol, 2023, 64(12): 3074-3084.
30. Lin R, Zheng Y, Lv F, et al. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules. Med Phys, 2023, 50(5): 2835-2843.
31. Warkentin M T, Al-Sawaihey H, Lam S, et al. Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches. Thorax, 2024, 79(4): 307-315.
32. Deng H, Zhang J, Li J, et al. Clinical features and radiological characteristics of pulmonary cryptococcosis. J Int Med Res, 2018, 46(7): 2687-2695.
33. Qu Y, Liu G, Ghimire P, et al. Primary pulmonary cryptococcosis: evaluation of CT characteristics in 26 immunocompetent Chinese patients. Acta Radiol, 2012, 53(6): 668-674.
34. Hu B, Xia W, Piao S, et al. A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test. Transl Lung Cancer Res, 2023, 12(8): 1790-801.
35. Li S, Zhang G, Yin Y, et al. One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed. Comput Med Imaging Graph, 2021, 94: 102009.
36. Larue R T H M, van Timmeren J E, de Jong E E C, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol, 2017, 56(11): 1544-1553.
37. Garau N, Paganelli C, Summers P, et al. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis. Med Phys, 2020, 47(9): 4125-4136.
38. Grosu H B, Manzanera A, Shivakumar S, et al. Survival disparities following surgery among patients with different histological types of non-small cell lung cancer. Lung Cancer, 2020, 140: 55-58.
39. Pei G, Wang D, Sun K, et al. Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study. Front Oncol, 2023, 13: 1224455.
40. Choi W, Liu C J, Alam S R, et al. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma. Comput Struct Biotechnol J, 2023, 21: 5601-5608.
41. Yotsukura M, Asamura H, Motoi N, et al. Long-term prognosis of patients with resected adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung. J Thorac Oncol, 2021, 16(8): 1312-1320.
42. Ito M, Miyata Y, Kushitani K, et al. Pathological high malignant grade is higher risk of recurrence in pN0M0 invasive lung adenocarcinoma, even with small invasive size. Thorac Cancer, 2021, 12(23): 3141-3149.
43. Wu F, Tian S P, Jin X, et al. CT and histopathologic characteristics of lung adenocarcinoma with pure ground-glass nodules 10 mm or less in diameter. Eur Radiol, 2017, 27(10): 4037-4043.
44. Kodama K, Higashiyama M, Yokouchi H, et al. Natural history of pure ground-glass opacity after long-term follow-up of more than 2 years. Ann Thorac Surg, 2002, 73(2): 386-393.
45. Eguchi T, Kondo R, Kawakami S, et al. Computed tomography attenuation predicts the growth of pure ground-glass nodules. Lung Cancer, 2014, 84(3): 242-247.
46. Yang Y, Li K, Sun D, et al. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as pure ground-glass nodules: differentiation using enhanced dual-source dual-energy CT. AJR Am J Roentgenol, 2019, 213(3): 114-122.
47. 杨延涛, 王维, 杨逸辰, 等. CT定量和定性特征联合预测肺磨玻璃结节浸润程度555例的回顾性队列研究. 中国胸心血管外科临床杂志, 2024, 31(1): 51-58.
48. Feng H, Shi G, Xu Q, et al. Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas. Insights Imaging, 2023, 14(1): 24.
49. Pan Z, Hu G, Zhu Z, et al. Predicting invasiveness of lung adenocarcinoma at chest CT with deep learning ternary classification models. Radiology, 2024, 311(1): e232057.
50. Han Y B, Kim H, Mino-Kenudson M, et al. Tumor spread through air spaces (STAS): prognostic significance of grading in non-small cell lung cancer. Mod Pathol, 2021, 34(3): 549-561.
51. Feng Y, Ding H, Huang X, et al. Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma. NPJ Precis Oncol, 2024, 8(1): 173.
52. Xu J, Liu L, Ji Y, et al. Enhanced CT-based intratumoral and peritumoral radiomics nomograms predict high-grade patterns of invasive lung adenocarcinoma. Acad Radiol, 2025, 32(1): 482-492.
53. Altorki N, Wang X, Kozono D, et al. Lobar or sublobar resection for peripheral stage IA non-small-cell lung cancer. N Engl J Med, 2023, 388(6): 489-498.
54. Mahvi D A, Liu R, Grinstaff M W, et al. Local cancer recurrence: the realities, challenges, and opportunities for new therapies. CA Cancer J Clin, 2018, 68(6): 488-505.
55. Chen K, Yang A, Carbone D P, et al. Spatiotemporal genomic analysis reveals distinct molecular features in recurrent stage I non-small cell lung cancers. Cell Rep, 2022, 40(2): 111047.
56. Khorrami M, Bera K, Leo P, et al. Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer, 2020, 142: 90-97.
57. Shimada Y, Kudo Y, Maehara S, et al. Radiomics with artificial intelligence for the prediction of early recurrence in patients with clinical stage IA lung cancer. Ann Surg Oncol, 2022, 29(13): 8185-8193.
58. Mattonen S A, Davidzon G A, Bakr S, et al. [18F] FDG positron emission tomography (PET) tumor and penumbra imaging features predict recurrence in non–small cell lung cancer. Tomography, 2019, 5(1): 145-153.
59. Bove S, Fanizzi A, Fadda F, et al. A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region. PLoS One, 2023, 18(5): e0285188.
60. Vaidya P, Bera K, Gupta A, et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable Non-Small Cell Lung Cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health, 2020, 2(3): 116-128.
61. Kratz J R, He J, Van Den Eeden S K, et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet, 2012, 379(9818): 823-832.
62. Zhu C Q, Ding K, Strumpf D, et al. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol, 2010, 28(29): 4417-4424.
63. Borgli H, Thambawita V, Smedsrud P H, et al. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data, 2020, 7(1): 283.
64. Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging, 2023, 14(1): 75.
65. Price W, Nicholson I. Black-box medicine. Harv JL & Tech, 2014, 28(2): 419-461.

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