A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study. (September 2020)

• Record Type: Journal Article
• Authors: Zhang, Liwen; Dong, Di; Zhang, Wenjuan; Hao, Xiaohan; Fang, Mengjie; Wang, Shuo; Li, Wuchao; Liu, Zaiyi; Wang, Rongpin; Zhou, Junlin; Tian, Jie
• Is Part Of: Radiotherapy and oncology. Volume 150(2020)
• Page Start: 73
• Page End: 80
• Publication Date: 2020-09
• Subjects: Gastric cancer -- Deep learning -- Overall survival -- Individualized treatment -- Computed tomography; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.06.010
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Deep learning model is a potential tool for risk prediction. Both radiomics model and deep learning model had prognostic values. The deep learning model can classify the patients into low- and high-risk groups. Individualized recommender is a potential tool to assist clinicians. Abstract: Background and purpose: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. Materials and methods: We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. Results: Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort ( P -value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort ( P -value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables ( P -value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). Conclusion: The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.