Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease.

ABSTRACT:

Objectives

Accurate preoperative staging of bladder cancer is essential for therapeutic decision-making, particularly in distinguishing between organ-confined (T2) and extravesical (T3) disease. This study aimed to develop a CT-based radiomics model to differentiate T2 from T3 tumors and to evaluate the impact of imaging timing relative to transurethral resection of the bladder (TURB) on model performance. Additionally, we assessed the added diagnostic value of integrating routine clinical biomarkers.

Methods

In this retrospective study, 97 patients with histologically confirmed bladder cancer who underwent TURB followed by contrast-enhanced CT were included. Tumor segmentation was performed using a semi-automated three-dimensional approach, and radiomic features were extrac

SUBMITTER: Lisson CG 

PROVIDER: S-EPMC12512170 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature