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Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network
Author(s): Kenny H. Cha; Lubomir M. Hadjiiski; Heang-Ping Chan; Ravi K. Samala; Richard H. Cohan; Elaine M. Caoili; Alon Z. Weizer; Ajjai Alva
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Paper 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.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751V (27 February 2018); doi: 10.1117/12.2292990
Show Author Affiliations
Kenny H. Cha, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Ravi K. Samala, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)
Alon Z. Weizer, Univ. of Michigan (United States)
Ajjai Alva, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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