Dual-channel deep learning captures intratumoural heterogeneity on CECT for preoperative risk stratification of thymic epithelial tumors.

ABSTRACT: Accurate preoperative risk stratification is critical for treating thymic epithelial tumors (TETs). This study developed a deep learning framework that combines a dual-channel convolutional neural network (CNN) with an adaptive dynamic clustering algorithm. The model was trained on contrast-enhanced CT (CECT) images from 336 multicenter TET patients. It first automates the segmentation of tumor subregions. Then, it constructs dual-channel input data containing the largest cross-sectional ROI and its corresponding habitat masks. Using transfer learning, we trained four CNN architectures for risk stratification. The DenseNet121-based dual-channel CNN achieved an AUC of 0.74-0.76 on an external test set. This performance surpassed conventional radiomics, single-channel CNN, and radiologists'

SUBMITTER: Pan X 

PROVIDER: S-EPMC12590028 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature