Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images

Abstract

Yu-Bo Zhang,1,2 Zhi-Qiang Chen,1,3 Yang Bu,2 Peng Lei,2 Wei Yang,4 Wei Zhang2 1School of Clinical Medicine, Ningxia Medical University, Yinchuan, 750004, People’s Republic of China; 2Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan, 750004, People’s Republic of China; 3Department of Hepatobiliary Surgery, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750002, People’s Republic of China; 4Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, People’s Republic of ChinaCorrespondence: Peng Lei, Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, No. 804 Shengli Nan Street, Yinchuan, 750004, People’s Republic of China, Email [email protected] Yang Bu, Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, No. 804 Shengli Nan Street, Yinchuan, 750004, People’s Republic of China, Email [email protected]: To construct a 2.5-dimensional (2.5D) CT radiomics-based deep learning (DL) model to predict early postoperative recurrence of hepatocellular carcinoma (HCC).Patients and Methods: We retrospectively analyzed the data of patients who underwent HCC resection at 2 centers. The 232 patients from center 1 were randomly divided into the training (162 patients) and internal validation cohorts (70 patients); 91 patients from center 2 formed the external validation cohort. We developed a 2.5D DL model based on a central 2D image with the maximum tumor cross-section and adjacent slices. Multiple views (transverse, sagittal, and coronal) and phases (arterial, plain, and portal) were incorporated. Multi-instance learning techniques were applied to the extracted data; the resulting comprehensive feature set was modeled using Logistic Regression, RandomForest, ExtraTrees, XGBoost, and LightGBM, with 5-fold cross validation and hyperparameter optimization with Grid-search. Receiver operating characteristic curves, calibration curves, DeLong test, and decision curve analysis were used to evaluate model performance.Results: The 2.5D DL model performed well in the training (AUC: 0.920), internal validation (AUC: 0.825), and external validation cohorts (AUC: 0.795). The 3D DL model performed well in the training cohort and poorly in the internal and external validation cohorts (AUCs: 0.751, 0.666, and 0.567, respectively), indicating overfitting. The combined model (2.5D DL+clinical) performed well in all cohorts (AUCs: 0.921, 0.835, 0.804). The Hosmer-Lemeshow test, DeLong test, and decision curve analysis confirmed the superiority of the combined model over the other signatures.Conclusion: The combined model integrating 2.5D DL and clinical features accurately predicts early postoperative HCC recurrence.Keywords: hepatocellular carcinoma, liver resection, deep learning, computed tomography, recurrence

Keywords

• hepatocellular carcinoma
• liver resection
• deep learning
• computed tomography
• recurrence