Bladder cancer treatment response assessment using deep learning in CT with transfer learning

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

doi:10.1117/12.2254977

3 March 2017 Bladder cancer treatment response assessment using deep learning in CT with transfer learning

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

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013404 (2017) https://doi.org/10.1117/12.2254977
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

We are developing a CAD system for bladder cancer treatment response assessment in CT. We compared the performance of the deep-learning convolution neural network (DL-CNN) using different network sizes, and with and without transfer learning using natural scene images or regions of interest (ROIs) inside and outside the bladder. The DL-CNN was trained to identify responders (T0 disease) and non-responders to chemotherapy. ROIs were extracted from segmented lesions in pre- and post-treatment scans of a patient and paired to generate hybrid pre-post-treatment paired ROIs. The 87 lesions from 82 patients generated 104 temporal lesion pairs and 6,700 pre-post-treatment paired ROIs. Two-fold cross-validation and receiver operating characteristic analysis were performed and the area under the curve (AUC) was calculated for the DL-CNN estimates. The AUCs for prediction of T0 disease after treatment were 0.77±0.08 and 0.75±0.08, respectively, for the two partitions using DL-CNN without transfer learning and a small network, and were 0.74±0.07 and 0.74±0.08 with a large network. The AUCs were 0.73±0.08 and 0.62±0.08 with transfer learning using a small network pre-trained with bladder ROIs. The AUC values were 0.77±0.08 and 0.73±0.07 using the large network pre-trained with the same bladder ROIs. With transfer learning using the large network pretrained with the Canadian Institute for Advanced Research (CIFAR-10) data set, the AUCs were 0.72±0.06 and 0.64±0.09, respectively, for the two partitions. None of the differences in the methods reached statistical significance. Our study demonstrated the feasibility of using DL-CNN for the estimation of treatment response in CT. Transfer learning did not improve the treatment response estimation. The DL-CNN performed better when transfer learning with bladder images was used instead of natural scene images.

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Kenny H. Cha, Lubomir M. Hadjiiski, Heang-Ping Chan, Ravi K. Samala, Richard H. Cohan, Elaine M. Caoili, Chintana Paramagul, Ajjai Alva, and Alon Z. Weizer "Bladder cancer treatment response assessment using deep learning in CT with transfer learning", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013404 (3 March 2017); https://doi.org/10.1117/12.2254977

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