A Hybrid Machine Learning Model Based on Semantic Information Can Optimize Treatment Decision for Naive Single 3-5-cm HCC Patients.

ABSTRACT:

Background

Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment.

Purpose

The aim of the study was to build a hybrid machine learning model to recommend optimized first treatment (laparoscopic hepatectomy [LH] or microwave ablation [MWA]) for naïve single 3-5-cm HCC patients based on early recurrence (ER, ≤2 years) probability.

Methods

This retrospective study collected 20 semantic variables of 582 patients (LH: 300, MWA: 282) from 13 hospitals with at least 24 months follow-up. Both groups were divided into training, validation, and test set, respectively. Five algorithms (logistics regression, random forest, neural network, stochastic gradient boosting, and eXtreme Gradient Boosting [XGB]) were used for model buildi

SUBMITTER: Ding W 

PROVIDER: S-EPMC9218628 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature