Non-Invasive Tumor Budding Evaluation and Correlation with Treatment Response in Bladder Cancer: A Multi-Center Cohort Study.
Authors
Affiliations (6)
Affiliations (6)
- Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, 600th Tianhe Road, Guangzhou, 510630, P. R. China.
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, P. R. China.
- Department of Urology, Yan'an Hospital Affiliated to Kunming Medical University, Kunming Medical University, Kunming, 650051, P. R. China.
- Department of Urology, the Second Hospital of Dalian Medical University, Dalian Medical University, Dalian, 116027, P. R. China.
- Department of Urology, Henan Provincial People's Hospital, Zhengzhou, 450003, P. R. China.
- Department of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, 1st Youyi Road, Chongqing, 400016, P. R. China.
Abstract
The clinical benefits of neoadjuvant chemoimmunotherapy (NACI) are demonstrated in patients with bladder cancer (BCa); however, more than half fail to achieve a pathological complete response (pCR). This study utilizes multi-center cohorts of 2322 patients with pathologically diagnosed BCa, collected between January 1, 2014, and December 31, 2023, to explore the correlation between tumor budding (TB) status and NACI response and disease prognosis. A deep learning model is developed to noninvasively evaluate TB status based on CT images. The deep learning model accurately predicts the TB status, with area under the curve values of 0.932 (95% confidence interval: 0.898-0.965) in the training cohort, 0.944 (0.897-0.991) in the internal validation cohort, 0.882 (0.832-0.933) in external validation cohort 1, 0.944 (0.908-0.981) in the external validation cohort 2, and 0.854 (0.739-0.970) in the NACI validation cohort. Patients predicted to have a high TB status exhibit a worse prognosis (p < 0.05) and a lower pCR rate of 25.9% (7/20) than those predicted to have a low TB status (pCR rate: 73.9% [17/23]; p < 0.001). Hence, this model may be a reliable, noninvasive tool for predicting TB status, aiding clinicians in prognosis assessment and NACI strategy formulation.