Automatic staging of bladder cancer on CT urography

Sankeerth S. Garapati; Lubomir M. Hadjiiski; Kenny H. Cha; Heang-Ping Chan; Elaine M. Caoili M.D.; Richard H. Cohan M.D.; Alon Weizer; Ajjai Alva; Chintana Paramagul; Jun Wei; Chuan Zhou

doi:10.1117/12.2217004

24 March 2016 Automatic staging of bladder cancer on CT urography

Sankeerth S. Garapati, Lubomir M. Hadjiiski, Kenny H. Cha, Heang-Ping Chan, Elaine M. Caoili M.D., Richard H. Cohan M.D., Alon Weizer, Ajjai Alva, Chintana Paramagul, Jun Wei, Chuan Zhou

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Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851G (2016) https://doi.org/10.1117/12.2217004
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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 (A_z). The average training A_z was 0.97 and the test A_z 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.

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Sankeerth S. Garapati, Lubomir M. Hadjiiski, Kenny H. Cha, Heang-Ping Chan, Elaine M. Caoili M.D., Richard H. Cohan M.D., Alon Weizer, Ajjai Alva, Chintana Paramagul, Jun Wei, and Chuan Zhou "Automatic staging of bladder cancer on CT urography", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851G (24 March 2016); https://doi.org/10.1117/12.2217004

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KEYWORDS

Bladder cancer

Bladder

Cancer

Image segmentation

Feature extraction

Decision support systems

Machine learning

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