The application of explainable deep-radiomics in lung cancer research: Method comparison and analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Yusen , GUO Chao ,  LI Shanqing

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China;

Corresponding?author：

LI Shanqing, Email: lishanqing@pumch.cn

Keywords：

Artificial intelligence; lung neoplasms; precision medicine; radiomics; deep learning; non-fully supervised learning; model interpretability

DOI：

10.7507/1007-4848.202603088

Video：

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Nowadays, lung cancer is the most common and lethal invasive tumor type in Chinese population, challenging overall health level. However, personalized early-stage treatment is currently still not widely implemented, and the choice of treatment highly depends on experience of physician. Based on deep learning and radiomics principles, deep-radiomics is important for establishing objective and promotable precision medicine plans. Among all aspects, the explainability of a model is critical for its usage in clinical practice. This paper discusses the technical aspects of explainable deep-radiomics in lung cancer, and analyzes challenges we are facing. Non-fully supervised learning methods, as a current hotspot in deep learning technology, can construct more trustworthy and practically valuable deep learning models through the co-design method of performance-interpretability. Medical artificial intelligence faces three core challenges in transitioning from the laboratory to hospitals: high-level cognitive demands, data privacy and generalization capabilities, and regulatory compliance. However, with appropriate design, non-fully supervised learning holds the greatest potential to bridge the gap between design and application, enabling broader adoption.

Citation： WANG Yusen, GUO Chao, LI Shanqing. The application of explainable deep-radiomics in lung cancer research: Method comparison and analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2026, 33(7): 1034-1042. doi: 10.7507/1007-4848.202603088 Copy

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23.	Wang C, Ma J, Shao J, et al. Non-invasive measurement using deep learning algorithm based on multi-source features fusion to predict PD-L1 expression and survival in NSCLC. Front Immunol, 2022, 13: 828560.
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27.	Wang Y, Gupta A, Tushar FI, et al. Concordance-based predictive uncertainty (CPU)-index: proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT. Artif Intell Med, 2025, 160: 103055.
28.	Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
29.	Paul R, Schabath M, Gillies R, et al. Convolutional neural network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Comput Biol Med, 2020, 122: 103882.
30.	Hunter B, Chen M, Ratnakumar P, et al. A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules. EBioMedicine, 2022, 86: 104344.
31.	Cho HH, Lee HY, Kim E, et al. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol, 2021, 4(1): 1286.
32.	Zhang K, Qi S, Cai J, et al. Content-based image retrieval with a convolutional siamese neural network: distinguishing lung cancer and tuberculosis in CT images. Comput Biol Med, 2022, 140: 105096.
33.	Pedrycz W. Algorithms of fuzzy clustering with partial supervision. Pattern Recognit Lett, 1985, 3(1): 13-20.
34.	Zech JR, Badgeley MA, Liu M, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med, 2018, 15(11): e1002683.
35.	Dietterich TG, Lathrop RH, Lozano-Pérez T. Solving the multiple instance problem with axis-parallel rectangles. Artif Intell, 1997, 89(1-2): 31-71.
36.	Li R, Mai Z, Zhang Z, et al. TransCAM: transformer attention-based CAM refinement for weakly supervised semantic segmentation. J Vis Commun Image Represent, 2023, 92: 103800.
37.	MacQueen J. Some methods for classification and analysis of multivariate observations. Le Cam LM, Neyman J, editors. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967: 281-297.
38.	Shen Z, Cao P, Yang J, et al. WS-LungNet: a two-stage weakly-supervised lung cancer detection and diagnosis network. Comput Biol Med, 2023, 154: 106587.
39.	Ji Y, Ge R, Meng W, et al. Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics. Front Oncol, 2025, 15: 1646851.
40.	Hu J, Cheng R, Quan M, et al. Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models. Eur J Nucl Med Mol Imaging, 2025, 52(10): 3792-3806.
41.	Sun W, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
42.	Huang X, Huang X, Wang K, et al. 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. BMC Med Imaging, 2025, 25(1): 225.
43.	Tozuka R, Kadoya N, Yasunaga A, et al. Fractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework. Jpn J Radiol, 2026, 44(1): 183-195.
44.	Babic B, Gerke S, Evgeniou T, et al. Beware explanations from AI in health care. Science, 2021, 373(6552): 284-286.
45.	Liu L, Wang X, Huang S, et al. Identifying non-small cell lung cancer subtypes by a hybrid representative causal network with computed tomography images. Eng Appl Artif Intell, 2026, 174: 114540.
46.	U. S. Food and Drug Administration. Transparency for machine learning-enabled medical devices: guiding principles. 2021.

1. Vinod SK. Decision making in lung cancer—how applicable are the guidelines? Clin Oncol, 2015, 27(2): 125-131.
2. Mokhles S, Maat APWM, Aerts JGJV, et al. Opinions of lung cancer clinicians on shared decision making in early-stage non-small-cell lung cancer. Interact Cardiovasc Thorac Surg, 2017, 25(2): 278-284.
3. LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324.
4. Holzinger A, Langs G, Denk H, et al. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov, 2019, 9(4): e1312.
5. Anton J, Castelli L, Chan MF, et al. How well do self-supervised models transfer to medical imaging? J Imaging, 2022, 8(12): 320.
6. Zhu P, Ogino M. Guideline-based additive explanation for computer-aided diagnosis of lung nodules. In: Suzuki K, Reyes M, Syeda-Mahmood T, et al, Editor-in-Chief. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support: Vol. 11797. Cham: Springer International Publishing, 2019: 39-47.
7. Kindermans PJ, Hooker S, Adebayo J, et al. The (un)reliability of saliency methods. arXiv, 2017: 1711.00867.
8. Salimi Y, Hajianfar G, Mansouri Z, et al. Organomics: a concept reflecting the importance of PET/CT healthy organ radiomics in non-small cell lung cancer prognosis prediction using machine learning. Clin Nucl Med, 2024, 49(10): 899-908.
9. Ferretti M, Corino VDA. Integrating radiomic and 3D autoencoder-based features for non-small cell lung cancer survival analysis. Comput Methods Programs Biomed, 2025, 258: 108496.
10. Amini M, Hajianfar G, Salimi Y, et al. MetaPredictomics: a comprehensive approach to predict postsurgical non-small cell lung cancer recurrence using clinicopathologic, radiomics, and organomics data. Clin Nucl Med, 2025, 50(12): 1130-1143.
11. Lu HJ, Ma HF, Chen DG, et al. Prediction of prognosis for non-small cell lung cancer after targeted therapy based on CT imaging and dense convolutional sparse coding. IEEE Sens J, 2025, 25(14): 26956-26969.
12. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. arXiv, 2014: 1206.5538.
13. Chu SM. Audio-visual speech modeling using coupled hidden Markov models. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2009: 1125-1128.
14. Singh SP. Transfer of learning by composing solutions of elemental sequential tasks. Mach Learn, 1992, 8(3-4): 323-339.
15. Xing EP, Jordan MI, Russell SJ, et al. Distance metric learning with application to clustering with side-information. Becker S, Thrun S, Obermayer K, editors. Advances in Neural Information Processing Systems 15. Cambridge: MIT Press, 2003: 505-512.
16. Ye G, Wei Z, Han C, et al. AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study. eClinicalMedicine, 2025, 89: 103551.
17. Yin X, Lu Y, Cui Y, et al. CT-based radiomics-deep learning model predicts occult lymph node metastasis in early-stage lung adenocarcinoma patients: a multicenter study. Chin J Cancer Res, 2025, 37(1): 118-131.
18. Tao J, Liang C, Yin K, et al. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer. Diagn Interv Imaging, 2022, 103(11): 535-544.
19. Zhao J, Wang T, Wang B, et al. Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage ⅠA lung adenocarcinoma: a multicenter study. J Cardiothorac Surg, 2025, 20(1): 246.
20. Jin W, Shen L, Tian Y, et al. Improving the prediction of spreading through air spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study. Biomark Res, 2023, 11(1): 102.
21. Jin W, Tian Y, Xuzhang W, et al. Non-linear modifications enhance prediction of pathological response to pre-operative PD-1 blockade in lung cancer: a longitudinal hybrid radiological model. Pharmacol Res, 2023, 198: 106992.
22. Wang C, Ma J, Shao J, et al. Predicting EGFR and PD-L1 status in NSCLC patients using multitask AI system based on CT images. Front Immunol, 2022, 13: 813072.
23. Wang C, Ma J, Shao J, et al. Non-invasive measurement using deep learning algorithm based on multi-source features fusion to predict PD-L1 expression and survival in NSCLC. Front Immunol, 2022, 13: 828560.
24. Masuda T, Kawahara D, Daido W, et al. A radiomics-based machine learning model for predicting pneumonitis during durvalumab treatment in locally advanced NSCLC. AI, 2026, 7(1): 32.
25. Zhong Y, Cai C, Chen T, et al. PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer. Nat Commun, 2023, 14(1): 7513.
26. Zhong Y, Cai C, Chen T, et al. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stageⅠinvasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study. Eur J Nucl Med Mol Imaging, 2024, 51(2): 521-534.
27. Wang Y, Gupta A, Tushar FI, et al. Concordance-based predictive uncertainty (CPU)-index: proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT. Artif Intell Med, 2025, 160: 103055.
28. Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
29. Paul R, Schabath M, Gillies R, et al. Convolutional neural network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Comput Biol Med, 2020, 122: 103882.
30. Hunter B, Chen M, Ratnakumar P, et al. A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules. EBioMedicine, 2022, 86: 104344.
31. Cho HH, Lee HY, Kim E, et al. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol, 2021, 4(1): 1286.
32. Zhang K, Qi S, Cai J, et al. Content-based image retrieval with a convolutional siamese neural network: distinguishing lung cancer and tuberculosis in CT images. Comput Biol Med, 2022, 140: 105096.
33. Pedrycz W. Algorithms of fuzzy clustering with partial supervision. Pattern Recognit Lett, 1985, 3(1): 13-20.
34. Zech JR, Badgeley MA, Liu M, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med, 2018, 15(11): e1002683.
35. Dietterich TG, Lathrop RH, Lozano-Pérez T. Solving the multiple instance problem with axis-parallel rectangles. Artif Intell, 1997, 89(1-2): 31-71.
36. Li R, Mai Z, Zhang Z, et al. TransCAM: transformer attention-based CAM refinement for weakly supervised semantic segmentation. J Vis Commun Image Represent, 2023, 92: 103800.
37. MacQueen J. Some methods for classification and analysis of multivariate observations. Le Cam LM, Neyman J, editors. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967: 281-297.
38. Shen Z, Cao P, Yang J, et al. WS-LungNet: a two-stage weakly-supervised lung cancer detection and diagnosis network. Comput Biol Med, 2023, 154: 106587.
39. Ji Y, Ge R, Meng W, et al. Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics. Front Oncol, 2025, 15: 1646851.
40. Hu J, Cheng R, Quan M, et al. Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models. Eur J Nucl Med Mol Imaging, 2025, 52(10): 3792-3806.
41. Sun W, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
42. Huang X, Huang X, Wang K, et al. 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. BMC Med Imaging, 2025, 25(1): 225.
43. Tozuka R, Kadoya N, Yasunaga A, et al. Fractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework. Jpn J Radiol, 2026, 44(1): 183-195.
44. Babic B, Gerke S, Evgeniou T, et al. Beware explanations from AI in health care. Science, 2021, 373(6552): 284-286.
45. Liu L, Wang X, Huang S, et al. Identifying non-small cell lung cancer subtypes by a hybrid representative causal network with computed tomography images. Eng Appl Artif Intell, 2026, 174: 114540.
46. U. S. Food and Drug Administration. Transparency for machine learning-enabled medical devices: guiding principles. 2021.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

The application of explainable deep-radiomics in lung cancer research: Method comparison and analysis

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