A deep learning-based psi CT network effectively predicts early recurrence after hepatectomy in HCC patients.

backgroundHepatocellular carcinoma (HCC) exhibits a high recurrence rate, and early recurrence significantly jeopardizes patient prognosis, necessitating reliable methods for early recurrence prediction.

methodsUtilizing multi-institutional data and integrating deep learning (DL) techniques, we established a neural network based on DenseNet capable of concurrently processing patients' triphasic enhanced CT scans. By incorporating an attention mechanism, the model automatically focuses on regions that significantly impact patient survival. Performance metrics were first evaluated using the concordance index (C-index), calibration curves, and decision curves based on the training and validation cohorts. Finally, class activation map (CAM) techniques were employed to visualize the regions of interest identified by the model. After model construction, five-fold cross-validation was performed to assess overfitting risks and further evaluate model stability.

resultsWe retrospectively collected data from 302 cases across five centers, including patients who underwent Partial Hepatectomy between December 2016 and December 2022. During model development, 180 patients from Institution I formed the training cohort, while the remaining patients comprised the validation cohort. The area under the ROC curve (AUC) for two-year outcomes was 0.797 in the validation cohort. Calibration curves, survival curves, and decision curve analysis (DCA) demonstrated the model's robust performance. CAMs revealed that the model primarily focuses on intra-abdominal solid organs, consistent with clinical experience. After model development, datasets were merged for cross-validation. The best model achieved a C-index of 0.774 in the validation cohort, with five-fold cross-validation yielding an average C-index of 0.778. The 95% confidence interval (CI) for the C-index, derived from cross-validation, ranged from 0.762 to 0.793.

conclusionOur DL-based enhanced CT network shows promise in predicting early recurrence in patients, representing a potential new strategy for early recurrence prediction in HCC.