Machine Learning-Based Radiomics for Bladder Cancer Staging: Evaluating the Role of Imaging Timing in Differentiating T2 from T3 Disease

Christoph G Lisson^1,2*Friedemann Zengerling³Luisa Gallee¹Konstantin Müller¹Sabitha Manoj¹Hanna Stöckl³Christian Bolenz³Meinrad Beer¹Michael Götz¹Catharina Sylvia Lisson^1,2*

• ¹Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Ulm, Germany
• ²University of Ulm, Ulm, Baden-Wurttemberg, Germany
• ³Department of Urology, University Hospital of Ulm, Ulm, Germany

Objectives: Accurate preoperative staging of bladder cancer is essential for therapeutic decision-making, particularly in distinguishing between organconfined (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 threedimensional approach, and radiomic features were extracted according to IBSI standards. A random forest classifier was trained to distinguish between T2 and T3 tumors. Patients were stratified according to the interval between TURB and CT imaging (≤14 days vs >14 days). Performance metrics were assessed for both radiomics-only and combined clinical-radiomics models. Clinical variables included preoperative creatinine, hemoglobin, arterial hypertension, diabetes mellitus, smoking status, and tumor size.Results: The radiomics-only model achieved an AUC of 0.68 in Cohort 1 (≤14 days post-TURB). In Cohort 2 (>14 days post-TURB), model performance improved with an AUC of 0.80. The combined clinical-radiomics model further enhanced performance, yielding an AUC of 0.76 in Cohort 1 and 0.82 in Cohort 2. Delayed imaging was associated with increased radiomic feature stability and improved classification accuracy, suggesting a potential benefit of temporal separation from post-surgical tissue changes.Conclusion: This study demonstrates the feasibility of CT-based radiomics using full-volume 3D tumor segmentation to distinguish between T2 and T3 bladder cancer. The integration of clinical biomarkers and consideration of imaging timing significantly improved model performance. These findings support the development of temporally optimized, multimodal prediction models for individualized bladder cancer staging and treatment planning.