A New Model to Predict Survival Time in Patients With Hepatocellular Carcinoma With BCLC Advanced Stage

TL;DR

The paper introduces a new survival prediction model for advanced hepatocellular carcinoma patients to guide treatment decisions in the era of multimolecular targeted therapies.

Contribution

A new survival prediction model for hepatocellular carcinoma patients using clinical and biomarker data in the multimolecular targeted agents era.

Findings

A Weibull model provided the best fit for survival prediction using clinical and biomarker variables.

Two new models were developed, with and without prothrombin time and des-γ-carboxyprothrombin.

The model aids in planning and evaluating sequential therapies for advanced hepatocellular carcinoma.

Abstract

The variety of treatments available makes it difficult to determine whether a treatment would be effective for an individual case in advanced‐stage hepatocellular carcinoma. We aimed to establish a new model of survival prediction in patients to establish standards in the recent and coming multimolecular targeted agents era. This analysis was prepared using a data set of 518 patients diagnosed with hepatocellular carcinoma prior to 2017. Multiple regression analysis showed the size of the largest tumor nodule, the number of nodules, macrovascular invasion, extrahepatic metastasis, total bilirubin, albumin, prothrombin time (PT), α‐fetoprotein, and des‐γ‐carboxyprothrombin (DCP) as independent predictors of survival. A Weibull model had the best fit and, based on these predictors, we established two new predicted survival models with or without incorporating PT and DCP. This model is useful for planning and evaluating the efficacy of recent sequential therapies in the multimolecular targeted agents era.

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