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Proceedings Paper

Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system
Author(s): Dhanuj Gandikota; Lubomir Hadjiiski; Heang-Ping Chan; Kenny H. Cha; Ravi Samala; Elaine M. Caoili; Richard H. Cohan; Alon Weizer; Ajjai Alva; Chintana Paramagul; Jun Wei; Chuan Zhou
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Paper Abstract

Stage T2 is the clinical threshold for deciding whether to treat bladder cancer with neoadjuvant chemotherapy. In this study we refined a radiomics-based decision support system (CDSS-S) to aid clinicians in staging of bladder cancer in CT urography (CTU). To train the CDSS-S, we used a data set of 84 bladder cancers from 76 CTU clinically staged cases, 43 cancers were below stage T2, and 41 were stage T2 or above. An independent test set comprising of 82 bladder cancers from 80 CTU clinically staged cases that were staged as T2 or above were also collected. Our Auto- Initialized Cascaded Level Sets (AI-CALS) segmentation pipeline was utilized to segment the lesions from which radiomics features were extracted. The training set was split on 2 balanced partitions. Four classifiers were studied: linear discriminant analysis (LDA), support vector machines (SVM), back-propagation neural networks (BPNN), and random forest (RAF) classifiers. Based on the likelihood scores for a training set, the decision threshold providing the highest classification accuracy for each classifier was determined. The classifier with the fixed decision threshold was then applied to the test set and the performance evaluated. The test classification accuracy for the LDA, SVM, BPNN, and RAF trained on Partition 1 was 0.95, 0.98, 0.88, and 0.89, respectively, and was 0.88, 0.94, 0.88, and 0.93, respectively, when trained on Partition 2. The test classification accuracy for the LDA, SVM, BPNN, and RAF trained on the entire training set was 0.94, 0.94, 0.94, and 0.89, respectively. The results show the potential of CDSS-S in bladder cancer stage assessment.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500W (13 March 2019); doi: 10.1117/12.2513566
Show Author Affiliations
Dhanuj Gandikota, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Kenny H. Cha, U.S. FDA (United States)
Ravi Samala, Univ. of Michigan (United States)
Elaine M. Caoili, Univ. of Michigan (United States)
Richard H. Cohan, Univ. of Michigan (United States)
Alon Weizer, Univ. of Michigan (United States)
Ajjai Alva, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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