Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors.

ABSTRACT:

Background

This study aimed to develop and evaluate a deep learning (DL) model utilizing contrast-enhanced computed tomography (CT) to assist radiologists in accurately stratifying the risk of thymic epithelial tumors (TETs) based on the World Health Organization (WHO) classification.

Methods

Involved retrospectively enrolling clinical data from 266 patients with histopathologically confirmed TETs from two centers: Center 1 (training set, n=205) and Center 2 (external testing set, n=61). Six DL models (DenseNet 121, ResNet 101, Inception V3, VGG 11, MobileNet V2, and ShuffleNet V2) were developed and evaluated using venous-phase CT images, alongside a traditional radiomic model using a support vector machine (SVM) for comparison. Diagnostic performance of junior and senior r

SUBMITTER: Zhao X 

PROVIDER: S-EPMC12350125 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature