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Proceedings Paper

Automatic staging of bladder cancer on CT urography
Author(s): Sankeerth S. Garapati; Lubomir M. Hadjiiski; Kenny H. Cha; Heang-Ping Chan; Elaine M. Caoili; Richard H. Cohan; Alon Weizer; Ajjai Alva; Chintana Paramagul; Jun Wei; Chuan Zhou
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Paper Abstract

Correct staging of bladder cancer is crucial for the decision of neoadjuvant chemotherapy treatment and minimizing the risk of under- or over-treatment. Subjectivity and variability of clinicians in utilizing available diagnostic information may lead to inaccuracy in staging bladder cancer. An objective decision support system that merges the information in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate and consistent staging assessments. In this study, we developed a preliminary method to stage bladder cancer. With IRB approval, 42 bladder cancer cases with CTU scans were collected from patient files. The cases were classified into two classes based on pathological stage T2, which is the decision threshold for neoadjuvant chemotherapy treatment (i.e. for stage ≥T2) clinically. There were 21 cancers below stage T2 and 21 cancers at stage T2 or above. All 42 lesions were automatically segmented using our auto-initialized cascaded level sets (AI-CALS) method. Morphological features were extracted, which were selected and merged by linear discriminant analysis (LDA) classifier. A leave-one-case-out resampling scheme was used to train and test the classifier using the 42 lesions. The classification accuracy was quantified using the area under the ROC curve (Az). The average training Az was 0.97 and the test Az was 0.85. The classifier consistently selected the lesion volume, a gray level feature and a contrast feature. This predictive model shows promise for assisting in assessing the bladder cancer stage.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851G (24 March 2016); doi: 10.1117/12.2217004
Show Author Affiliations
Sankeerth S. Garapati, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Kenny H. Cha, Univ. of Michigan (United States)
Heang-Ping Chan, 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. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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