Progress in the application of deep learning in prognostic models for non-small cell lung cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHONG Ruikang ^1,2 , LI Jinghua ^1,2 , LIN Ximing ^1,2 , FANG Xueni ^1,2 ,  HU Kaiwen ² , ZHOU Tian ²

1. Beijing University of Chinese Medicine, Beijing, 100105, P. R. China;
2. Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, 100078, P. R. China;

Corresponding author：

HU Kaiwen, Email: zhoutian_med@163.com

Keywords：

Deep learning; non-small cell lung cancer; prognostic model; precision medicine; review

DOI：

10.7507/1007-4848.202312004

Video：

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Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.

Citation： ZHONG Ruikang, LI Jinghua, LIN Ximing, FANG Xueni, HU Kaiwen, ZHOU Tian. Progress in the application of deep learning in prognostic models for non-small cell lung cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(9): 1345-1350. doi: 10.7507/1007-4848.202312004 Copy

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36.	Sturgeon KM, Deng L, Bluethmann SM, et al. A population-based study of cardiovascular disease mortality risk in US cancer patients. Eur Heart J, 2019, 40(48): 3889-3897.
37.	Chao H, Shan H, Homayounieh F, et al. Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun, 2021, 12(1): 2963.
38.	Liang B, Tian Y, Chen X, et al. Prediction of radiation pneumonitis with dose distribution: A convolutional neural network (CNN) based model. Front Oncol, 2020, 9: 1500.
39.	Cui S, Ten Haken RK, El Naqa I. Integrating multiomics information in deep learning architectures for joint actuarial outcome prediction in non-small cell lung cancer patients after radiation therapy. Int J Radiat Oncol Biol Phys, 2021, 110(3): 893-904.
40.	Bang YH, Choi YH, Park M, et al. Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation. Sci Rep, 2023, 13(1): 11501.
41.	Shorten C, Khoshgoftaar MT. A survey on image data augmentation for deep learning. J Big Data, 2019, 6: 60.
42.	Kwon HJ, Koo HI, Soh JW, et al. Inverse-based approach to explaining and visualizing convolutional neural networks. IEEE Trans Neural Netw Learn Syst, 2022, 33(12): 7318-7329.
43.	Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization. IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, Las Vegas, USA, 2016.

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. Fitzmaurice C, Akinyemiju TF, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2016: A systematic analysis for the global burden of disease study. JAMA Oncol, 2018, 4(11): 1553-1568.
3. Pao W, Girard N. New driver mutations in non-small-cell lung cancer. Lancet Oncol, 2011, 12(2): 175-180.
4. Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine. Nat Med, 2022, 28(1): 31-38.
5. Doppalapudi S, Qiu RG, Badr Y. Lung cancer survival period prediction and understanding: Deep learning approaches. Int J Med Inform, 2021, 148: 104371.
6. Obermeyer Z, Emanuel EJ. Predicting the future: Big data, machine learning, and clinical medicine. N Engl J Med, 2016, 375(13): 1216-1219.
7. Arimura H, Soufi M, Kamezawa H, et al. Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res, 2019, 60(1): 150-157.
8. Kontos D, Summers RM, Giger M. Special section guest editorial: Radiomics and deep learning. J Med Imaging (Bellingham), 2017, 4(4): 041301.
9. Howlader N, Forjaz G, Mooradian MJ, et al. The effect of advances in lung-cancer treatment on population mortality. N Engl J Med, 2020, 383(7): 640-649.
10. Miller KD, Nogueira L, Mariotto AB, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin, 2019, 69(5): 363-385.
11. Singhal S, Vachani A, Antin-Ozerkis D, et al. Prognostic implications of cell cycle, apoptosis, and angiogenesis biomarkers in non-small cell lung cancer: A review. Clin Cancer Res, 2005, 11(11): 3974-3986.
12. Chen W, Hou X, Hu Y, et al. A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer. Med Phys, 2021, 48(12): 7946-7958.
13. Lin T, Mai J, Yan M, et al. A nomogram based on CT deep learning signature: A potential tool for the prediction of overall survival in resected non-small cell lung cancer patients. Cancer Manag Res, 2021, 13: 2897-2906.
14. Caruso CM, Guarrasi V, Cordelli E, et al. A multimodal ensemble driven by multiobjective optimisation to predict overall survival in non-small-cell lung cancer. J Imaging, 2022, 8(11): 298.
15. Lu CF, Liao CY, Chao HS, et al. A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer. Cancer Imaging, 2023, 23(1): 9.
16. Deng K, Wang L, Liu Y, et al. A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage Ⅳ non-small cell lung cancer patients: A multicenter, prognostic study. EClinicalMedicine, 2022, 51: 101541.
17. Gainey JC, He Y, Zhu R, et al. Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer. Front Oncol, 2023, 13: 868471.
18. Lu L, Wang D, Wang L, et al. A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma. Eur Radiol, 2020, 30(7): 3614-3623.
19. Ahn Y, Lee SM, Choi S, et al. Automated CT quantification of interstitial lung abnormality and interstitial lung disease according to the Fleischner Society in patients with resectable lung cancer: Prognostic significance. Eur Radiol, 2023, 33(11): 8251-8262.
20. Zhu Y, Chen LL, Luo YW, et al. Prognostic impact of deep learning-based quantification in clinical stage 0-Ⅰlung adenocarcinoma. Eur Radiol, 2023, 33(12): 8542-8553.
21. Wang S, Rong R, Yang DM, et al. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Res, 2020, 80(10): 2056-2066.
22. Guo H, Diao L, Zhou X, et al. Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse. Transl Lung Cancer Res, 2021, 10(6): 2452-2474.
23. Litière S, Collette S, de Vries EG, et al. RECIST - learning from the past to build the future. Nat Rev Clin Oncol, 2017, 14(3): 187-192.
24. Li S, Li W, Ma T, et al. Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network. Front Immunol, 2022, 13: 1024707.
25. Li W, Fu S, Gao X, et al. Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network. Front Immunol, 2023, 14: 1141408.
26. Mehlen P, Puisieux A. Metastasis: A question of life or death. Nat Rev Cancer, 2006, 6(6): 449-458.
27. Wu J, Aguilera T, Shultz D, et al. Early-stage nonsmall cell lung cancer: Quantitative imaging characteristics of (18)F fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology, 2016, 81: 270-278.
28. Zhou H, Dong D, Chen B, et al. Diagnosis of distant metastasis of lung cancer: Based on clinical and radiomic features. Transl Oncol, 2018, 11(1): 31-36.
29. Tau N, Stundzia A, Yasufuku K, et al. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol, 2020, 215(1): 192-197.
30. Xu Y, Hosny A, Zeleznik R, et al. Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res, 2019, 25(11): 3266-3275.
31. Petitprez F, Vano YA, Becht E, et al. Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies. Cancer Immunol Immunother, 2018, 67(6): 981-988.
32. Qi C, Cai Y, Qian K, et al. GutMDisorder v2.0: A comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions. Nucleic Acids Res, 2023, 51(D1): D717-D722.
33. Wu K, Xu L, Cheng L. PAR2 promoter hypomethylation regulates PAR2 gene expression and promotes lung adenocarcinoma cell progression. Comput Math Methods Med, 2021, 2021: 5542485.
34. Albaradei S, Napolitano F, Thafar MA, et al. Meta cancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data. Comput Struct Biotechnol J, 2021, 19: 4404-4411.
35. Liu D, Yao L, Ding X, et al. Multi-omics immune regulatory mechanisms in lung adenocarcinoma metastasis and survival time. Comput Biol Med, 2023, 164: 107333.
36. Sturgeon KM, Deng L, Bluethmann SM, et al. A population-based study of cardiovascular disease mortality risk in US cancer patients. Eur Heart J, 2019, 40(48): 3889-3897.
37. Chao H, Shan H, Homayounieh F, et al. Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun, 2021, 12(1): 2963.
38. Liang B, Tian Y, Chen X, et al. Prediction of radiation pneumonitis with dose distribution: A convolutional neural network (CNN) based model. Front Oncol, 2020, 9: 1500.
39. Cui S, Ten Haken RK, El Naqa I. Integrating multiomics information in deep learning architectures for joint actuarial outcome prediction in non-small cell lung cancer patients after radiation therapy. Int J Radiat Oncol Biol Phys, 2021, 110(3): 893-904.
40. Bang YH, Choi YH, Park M, et al. Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation. Sci Rep, 2023, 13(1): 11501.
41. Shorten C, Khoshgoftaar MT. A survey on image data augmentation for deep learning. J Big Data, 2019, 6: 60.
42. Kwon HJ, Koo HI, Soh JW, et al. Inverse-based approach to explaining and visualizing convolutional neural networks. IEEE Trans Neural Netw Learn Syst, 2022, 33(12): 7318-7329.
43. Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization. IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, Las Vegas, USA, 2016.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Progress in the application of deep learning in prognostic models for non-small cell lung cancer

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