Enhancing prediction accuracy for muscle invasion in bladder cancer using a dual-energy CT-based interpretable model incorporating habitat radiomics and deep learning.

ABSTRACT:

Background

Accurately predicting muscle invasion status in bladder cancer (BCa) is essential for developing personalized treatment plans. This study integrates habitat analysis and 2.5-dimension (2.5D) deep learning (DL) to create an interpretable model using dual-energy computed tomography (DECT) images to enhance the preoperative assessment of muscle invasion status in BCa.

Methods

This study retrospectively recruited 200 BCa patients who underwent DECT urography and divided them into a training cohort (n = 140) and a test cohort (n = 60). Quantitative parameters from DECT images were measured, and independent predictors of muscle invasion were identified through stepwise regression analysis to develop a DECT model. Radiomics and 2.5D DL models were constructed using iodin

SUBMITTER: Du C 

PROVIDER: S-EPMC12667133 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature