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.