Abstract
PURPOSE: The study aimed to establish a prognostic prediction model and an artificial neural network (ANN) model to determine who will benefit from transarterial chemoembolization (TACE) monotherapy for patients with hepatocellular carcinoma (HCC) invading portal vein. METHODS: Treatment-naïve patients with HCC and portal vein invasion who were treated with TACE monotherapy at hospital A as training cohort and hospital B as validation cohort were included. The primary endpoint was overall survival (OS). In training cohort, independent risk factors associated with OS were identified by univariate and multivariate analysis. The prognostic prediction (PP) and ANN models based on the independent risk factors were established to find out who will benefit most from TACE monotherapy. The type of portal vein tumor thrombosis was classified based on the Cheng's Classification. The accuracy of the models was validated in validation cohort. RESULTS: Totally, 242 patients (training cohort: n = 159; validation cohort: n = 83) were included. The median OS was 7.1 and 8.5 months in training and validation cohort, respectively. In training cohort, the PP model was established based on the following five independent risk factors: Cheng's Classification, Eastern Cooperative Oncology Group score, maximum tumor size, number of HCC nodules, and Child-Pugh stage. PP score of 17.5 was identified as cut-off point and patients were divided into two groups by PP score <17.5 and >17.5 in survival benefit and prognostication (8.8 vs. 5.5 months; P < 0.001). These five factors were included and ranked based on the importance associated with OS in the ANN model. Both of the two models received high accuracy after validation. CONCLUSIONS: Portal vein invaded HCC patients with PP score <17.5 may benefit most from TACE monotherapy. For these patients, TACE monotherapy should be considered.