A Deep Learning Model Combining Multimodal Factors to Predict the Overall Survival of Transarterial Chemoembolization
Zhongqi Sun, Xin Li, Hongwei Liang, Zhongxing Shi, Hongjia Ren
Abstract
Background: To develop and validate an overall survival (OS) prediction model for transarterial chemoembolization (TACE). Methods: In this retrospective study, 301 patients with hepatocellular carcinoma (HCC) who received TACE from 2012 to 2015 were collected. The residual network was used to extract prognostic information from CT images, which was then combined with the clinical factors adjusted by COX regression to predict survival using a modified deep learning model (DLOP Combin ). The DLOP Combin model was compared with the residual network model (DLOP CTR ), multiple COX regression model (DLOP Cox ), Radiomic model (Radiomic), and clinical model. Results: In the validation cohort, DLOP Combin shows the highest TD AUC of all cohorts, which compared with Radiomic (TD AUC: 0.96vs 0.63) and clinical model (TD AUC: 0.96 vs 0.62) model. DLOP Combin showed significant difference in C index compared with DLOP CTR and DLOP Cox models ( P < 0.05). Moreover, the DLOP Combin showed good calibration and overall net benefit. Patients with DLOP Combin model score ≤ 0.902 had better OS (33 months vs 15.5 months, P < 0.0001). Conclusion: The deep learning model can effectively predict the patients’ overall survival of TACE. Keywords: deep learning, transarterial chemoembolization, hepatocellular carcinoma