Novel Model to Predict HCC Recurrence after Liver Transplantation Obtained Using Deep Learning: A Multicenter Study

SIMPLE SUMMARY: Although several models have been developed to extend the criteria for liver transplantation in hepatocellular carcinoma beyond the Milan criteria, there are still no standard criteria. This study aimed to develop and validate a novel model to predict hepatocellular carcinoma recurrence after liver transplantation by adopting artificial intelligence (MoRAL-AI). The MoRAL-AI showed significantly better discrimination (c-index = 0.75) than previous models in the independent validation cohort: the Milan (c-index = 0.64), MoRAL (c-index = 0.69), UCSF (c-index = 0.62), up-to-seven (c-index = 0.50), and Kyoto (c-index = 0.50) criteria (all p < 0.001). We assessed the weighted parameters for tumor recurrence in the MoRAL-AI with the deep learning method: tumor diameter, followed by alpha-fetoprotein, age, and PIVKA-II. ABSTRACT: Several models have been developed using conventional regression approaches to extend the criteria for liver transplantation (LT) in hepatocellular carcinoma (HCC) beyond the Milan criteria. We aimed to develop a novel model to predict tumor recurrence after LT by adopting artificial intelligence (MoRAL-AI). This study included 563 patients who underwent LT for HCC at three large LT centers in Korea. Derivation (n = 349) and validation (n = 214) cohorts were independently established. The primary outcome was time-to-recurrence after LT. A MoRAL-AI was derived from the derivation cohort with a residual block-based deep neural network. The median follow-up duration was 74.7 months (interquartile-range, 18.5–107.4); 204 patients (36.2%) had HCC beyond the Milan criteria. The optimal model consisted of seven layers including two residual blocks. In the validation cohort, the MoRAL-AI showed significantly better discrimination function (c-index = 0.75) than the Milan (c-index = 0.64), MoRAL (c-index = 0.69), University of California San Francisco (c-index = 0.62), up-to-seven (c-index = 0.50), and Kyoto (c-index = 0.50) criteria (all p < 0.001). The largest weighted parameter in the MoRAL-AI was tumor diameter, followed by alpha-fetoprotein, age, and protein induced by vitamin K absence-II. The MoRAL-AI had better predictability of tumor recurrence after LT than conventional models. The MoRAL-AI can also evolve with further data.