Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network

Kenny H. Cha; Lubomir M. Hadjiiski; Heang-Ping Chan; Ravi K. Samala; Richard H. Cohan; Elaine M. Caoili; Alon Z. Weizer; Ajjai Alva

doi:10.1117/12.2292990

27 February 2018 Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network

Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Ravi K. Samala, Richard H. Cohan, Elaine M. Caoili, Alon Z. Weizer, Ajjai Alva

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105751V (2018) https://doi.org/10.1117/12.2292990
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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% (AUC^0.9) 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 AUC^0.9s 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 AUC^0.9s 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.

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Citation Download Citation

Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Ravi K. Samala, Richard H. Cohan, Elaine M. Caoili, Alon Z. Weizer, and Ajjai Alva "Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751V (27 February 2018); https://doi.org/10.1117/12.2292990

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