We are developing a CAD system for bladder cancer treatment response assessment in CT. We trained a 2- Channel Deep-learning Convolution Neural Network (2Ch-DCNN) to identify responders (T0 disease) and nonresponders to chemotherapy. The 87 lesions from 82 cases generated 18,600 training paired ROIs that were extracted from segmented bladder lesions in the pre- and post-treatment CT scans and partitioned for 2-fold cross validation. The paired ROIs were input to two parallel channels of the 2Ch-DCNN. We compared the 2Ch-DCNN with our hybrid prepost- treatment ROI DCNN method and the assessments by 2 experienced abdominal radiologists. The radiologist estimated the likelihood of stage T0 after viewing each pre-post-treatment CT pair. Receiver operating characteristic analysis was performed and the area under the curve (AUC) and the partial AUC at sensitivity <90% (AUC0.9) were compared. The test AUCs were 0.76±0.07 and 0.75±0.07 for the 2 partitions, respectively, for the 2Ch-DCNN, and were 0.75±0.08 and 0.75±0.07 for the hybrid ROI method. The AUCs for Radiologist 1 were 0.67±0.09 and 0.75±0.07 for the 2 partitions, respectively, and were 0.79±0.07 and 0.70±0.09 for Radiologist 2. For the 2Ch-DCNN, the AUC0.9s were 0.43 and 0.39 for the 2 partitions, respectively, and were 0.19 and 0.28 for the hybrid ROI method. For Radiologist 1, the AUC0.9s were 0.14 and 0.34 for partition 1 and 2, respectively, and were 0.33 and 0.23 for Radiologist 2. Our study demonstrated the feasibility of using a 2Ch-DCNN for the estimation of bladder cancer treatment response in CT.