27 February 2018 Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network
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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% (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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenny H. Cha, Kenny H. Cha, Lubomir M. Hadjiiski, Lubomir M. Hadjiiski, Heang-Ping Chan, Heang-Ping Chan, Ravi K. Samala, Ravi K. Samala, Richard H. Cohan, Richard H. Cohan, Elaine M. Caoili, Elaine M. Caoili, Alon Z. Weizer, Alon Z. Weizer, Ajjai Alva, 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); doi: 10.1117/12.2292990; https://doi.org/10.1117/12.2292990
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