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

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.

Conference Presentation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available