A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy

Abstract

Background There is no satisfactory indicator for monitoring recurrence after resection of hepatocellular carcinoma (HCC). This retrospective study aimed to design and validate an HCC monitor recurrence (HMR) model for patients without metastasis after hepatectomy. Methods A training cohort was recruited from 1179 patients with HCC without metastasis after hepatectomy between February 2012 and December 2015. An HMR model was developed using an AdaBoost classifier algorithm. The factors included patient age, TNM staging, tumor size, and pre/postoperative dynamic variations of alpha-fetoprotein (AFP). The diagnostic efficacy of the model was evaluated based on the area under the receiver operating characteristic curves (AUCs). The model was validated using a cohort of 695 patients. Results In preoperative patients with positive or negative AFP, the AUC of the validation cohort in the HMR model was .8877, which indicated better diagnostic efficacy than that of serum AFP (AUC, .7348). The HMR model predicted recurrence earlier than computed tomography/magnetic resonance imaging did by 191.58 ± 165 days. In addition, the HMR model can predict the prognosis of patients with HCC after resection. Conclusions The HMR model established in this study is more accurate than serum AFP for monitoring recurrence after hepatectomy for HCC and can be used for real-time monitoring of the postoperative status in patients with HCC without metastasis.