Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.
Scientific reports. 2017 Aug 18*** epublish ***
Kenny H Cha, Lubomir Hadjiiski, Heang-Ping Chan, Alon Z Weizer, Ajjai Alva, Richard H Cohan, Elaine M Caoili, Chintana Paramagul, Ravi K Samala
Department of Radiology, The University of Michigan, Ann Arbor, Michigan, 48109, United States. ., Department of Radiology, The University of Michigan, Ann Arbor, Michigan, 48109, United States., Department of Urology, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, Michigan, 48109, United States., Department of Internal Medicine, Hematology-Oncology, The University of Michigan, Ann Arbor, Michigan, 48109, United States.