Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features.

ABSTRACT:

Objectives

To develop and validate nomograms combining radiomics and semantic features to identify the invasiveness and histopathological risk stratification of thymic epithelial tumors (TET) using contrast-enhanced CT.

Methods

This retrospective multi-center study included 224 consecutive cases. For each case, 6764 intratumor and peritumor radiomics features and 31 semantic features were collected. Multi-feature selections and decision tree models were performed on radiomics features and semantic features separately to select the most important features for Masaoka-Koga staging and WHO classification. The selected features were then combined to create nomograms for the two systems. The performance of the radiomics model, semantic model, and combined model was evaluated usin

SUBMITTER: Zhang L 

PROVIDER: S-EPMC11496418 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature