Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic study.

ABSTRACT:

Background

Our study aimed to develop a machine learning (ML) model utilizing grayscale ultrasound (US) to distinguish ≤3 cm small hepatocellular carcinoma (sHCC) from non-HCC lesions.

Methods

A total of 1052 patients with 1058 liver lesions ≤3 cm from 55 hospitals were collected between May 2017 and June 2021, and 756 liver lesions were randomly allocated into train and internal validation cohorts at a 8:2 ratio for the development and evaluation of ML models based on multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) methods (Model^U utilizing US imaging features; Model^UR adding US radiomics features; Model^URC employing clinical features further). The diagnostic performance of three models was assessed in external validation

SUBMITTER: Du Z 

PROVIDER: S-EPMC11872562 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature