Construction of a radiomics model based on CT imaging for predicting capsular invasion in thymomas.

ABSTRACT:

Objective

To develop a radiomics-based predictive model for capsular invasion in thymomas by applying machine learning algorithms to non-contrast and contrast-enhanced CT imaging. This study aimed to assess the influence of intratumoural and peritumoural regions on capsular invasion prediction and to compare the performance of models derived from these regions within the same dataset, thereby identifying the optimal predictive model.

Methods

Clinical and imaging data were retrospectively collected from 151 patients with thymoma who underwent treatment at Tianjin Chest Hospital between June 2018 and January 2025. Based on pathological findings, patients were categorised into capsular invasion and non-invasion groups and subsequently randomised into a training set (n = 

SUBMITTER: Liang S 

PROVIDER: S-EPMC12740908 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature