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Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features
Author(s): Marshall N. Gordon; Kenny H. Cha; Lubomir M. Hadjiiski; Heang-Ping Chan; Richard H. Cohan; Elaine M. Caoili; Chintana Paramagul; Ajjai Alva; Alon Z. Weizer
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Paper Abstract

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist’s semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist’s semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751Y (27 February 2018); doi: 10.1117/12.2294951
Show Author Affiliations
Marshall N. Gordon, Univ. of Michigan (United States)
Kenny H. Cha, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Ajjai Alva, Univ. of Michigan (United States)
Alon Z. Weizer, 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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