Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning_Journal of Biomedical Engineering

Authors：

HU Lunyu ^1,2 , XIA Wei ^1,2 , LI Qiong ³ ,  GAO Xin ^2,4

1. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China;
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China;
3. Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China;
4. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan 250101, P. R. China;

Corresponding author：

GAO Xin, Email: xingaosam@163.com

Keywords：

Computed tomography; Lung adenocarcinoma; Postoperative recurrence; Self-supervised learning; Multi-task learning

DOI：

10.7507/1001-5515.202309060

Video：

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Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative approach based on self-supervised pre-training and multi-task learning. We employed a self-supervised learning strategy known as “image transformation to image restoration” to pretrain a 3D-UNet network on publicly available lung CT datasets to extract generic visual features from lung images. Subsequently, we enhanced the network’s feature extraction capability through multi-task learning involving segmentation and classification tasks, guiding the network to extract image features relevant to RFS. Additionally, we designed a multi-scale feature aggregation module to comprehensively amalgamate multi-scale image features, and ultimately predicted the RFS risk score for lung adenocarcinoma with the aid of a feed-forward neural network. The predictive performance of the proposed method was assessed by ten-fold cross-validation. The results showed that the consistency index (C-index) of the proposed method for predicting RFS and the area under curve (AUC) for predicting whether recurrence occurs within three years reached 0.691 ± 0.076 and 0.707 ± 0.082, respectively, and the predictive performance was superior to that of existing methods. This study confirms that the proposed method has the potential of RFS prediction in lung adenocarcinoma patients, which is expected to provide a reliable basis for the development of individualized treatment plans.

Citation： HU Lunyu, XIA Wei, LI Qiong, GAO Xin. Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning. Journal of Biomedical Engineering, 2024, 41(2): 205-212. doi: 10.7507/1001-5515.202309060 Copy

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30.	Li F, Sone S, Abe H, et al. Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings. Radiology, 2004, 233(3): 793-798.
31.	Hattori A, Suzuki K, Matsunaga T, et al. Visceral pleural invasion is not a significant prognostic factor in patients with a part-solid lung cancer. Ann Thorac Surg, 2014, 98(2): 433-438.

1. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Center, 2024. DOI: 10.1016/j.jncc.2024.01.006.
2. Li M Y, Liu L Z, Dong M. Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis. Mol Cancer, 2021, 20: 1-22.
3. Bowes K, Jovanoski N, Brown A E, et al. Treatment patterns and survival of patients with locoregional recurrence in early-stage NSCLC: a literature review of real-world evidence. Med Oncol, 2022, 40(1): 4.
4. Woodard G A, Jones K D, Jablons D M, et al. Lung cancer staging and prognosis. Cancer Treat Res, 2016: 47-75.
5. Lee G, Lee H Y, Park H, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol, 2017, 86: 297-307.
6. Xie D, Wang T T, Huang S J, et al. Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma. Transl Lung Cancer Res, 2020, 9(4): 1112.
7. Kirienko M, Cozzi L, Antunovic L, et al. Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging, 2018, 45: 207-217.
8. Chen H, Liang M, Li X, et al. An individualised radiomics composite model predicting prognosis of stage 1 solid lung adenocarcinoma. Clin Radiol, 2020, 75(7): 562.e11-562.e19.
9. Wang T, Deng J, She Y, et al. Radiomics signature predicts the recurrence-free survival in stage I non-small cell lung cancer. Ann Thorac Surg, 2020, 109(6): 1741-1749.
10. Berenguer R, Pastor-Juan M D R, Canales-Vázquez J, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology, 2018, 288(2): 407-415.
11. Avanzo M, Stancanello J, Pirrone G, et al. Radiomics and deep learning in lung cancer. Strahlenther Onkol, 2020, 196: 879-887.
12. Gao Y, Zhou R, Lyu Q. Multiomics and machine learning in lung cancer prognosis. J Thorac Dis, 2020, 12(8): 4531-4535.
13. Cong L, Feng W, Yao Z, et al. Deep learning model as a new trend in computer-aided diagnosis of tumor pathology for lung cancer. J Cancer, 2020, 11(12): 3615-3622.
14. Wang P, Li Y, Reddy C K. Machine learning for survival analysis: A survey. ACM Comput Surv (CSUR), 2019, 51(6): 1-36.
15. Hosny A, Parmar C, Coroller T P, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
16. 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.
17. 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.
18. Cox D R. Regression models and life‐tables. J R Stat Soc: Ser B (Methodol), 1972, 34(2): 187-202.
19. Baek S, He Y, Allen B G, et al. Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep, 2019, 9(1): 17286.
20. Liu K, Li K, Wu T, et al. Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT. Eur Radiol, 2022, 32(2): 1065-1077.
21. Chen S, Ma K, Zheng Y. Med3D: Transfer learning for 3D medical image analysis. arXiv preprint arXiv: 2019: 1904.00625.
22. Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
23. Ishida H, Shimizu Y, Sakaguchi H, et al. Distinctive clinicopathological features of adenocarcinoma in situ and minimally invasive adenocarcinoma of the lung: a retrospective study. Lung Cancer, 2019, 129: 16-21.
24. Chen T, Liu S, Chang S, et al. Adversarial robustness: From self-supervised pre-training to fine-tuning// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 699-708.
25. Zhou Z, Sodha V, Pang J, et al. Models genesis. Med Image Anal, 2021, 67: 101840.
26. Setio A A A, 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. Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988.
28. Katzman J L, Shaham U, Cloninger A, et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol, 2018, 18(1): 1-12.
29. Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 618-626.
30. Li F, Sone S, Abe H, et al. Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings. Radiology, 2004, 233(3): 793-798.
31. Hattori A, Suzuki K, Matsunaga T, et al. Visceral pleural invasion is not a significant prognostic factor in patients with a part-solid lung cancer. Ann Thorac Surg, 2014, 98(2): 433-438.

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