A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma.

ABSTRACT:

Background

Due to organ shortage, liver transplantation (LT) in hepatocellular carcinoma (HCC) patients can only be offered subsidiary to other curative treatments, including liver resection (LR). We aimed at developing and validating a machine-learning algorithm (ML) to predict which patients are sufficiently treated by LR.

Methods

Twenty-six preoperatively available routine laboratory values along with standard clinical-pathological parameters [including the modified Glascow Prognostic Score (mGPS), the Kings Score (KS) and the Model of Endstage Liver Disease (MELD)] were retrieved from 181 patients who underwent partial LR due to HCC in non-cirrhosis or compensated cirrhosis from January 2007 through March 2018 at our institution. These data were processed using a Random

SUBMITTER: Schoenberg MB 

PROVIDER: S-EPMC7210189 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature