Deep Learning Models for Predicting the Survival of Patients with Hepatocellular Carcinoma Based on a Surveillance, Epidemiology, and End Results (SEER) Database Analysis

Background This study aims to develop and validate a predictive model for Hepatocellular Carcinoma (HCC) patients using deep learning algorithms and to explore its clinical applicability. Methods HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and 5-fold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. Results The study included 2,197 HCC patients, randomly divided into a training cohort (70%, n = 1,537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model provided more accurate and better-calibrated survival estimates for predicting 1-year, 3-year, and 5-year survival rates (AUC: 0.803–0.824). We deployed the NMTLR model as a web application for clinical practice. Conclusion The predictive model developed using the deep learning algorithm NMTLR demonstrated excellent performance in prognostication for Primary Hepatocellular Carcinoma.