Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system

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

doi:10.1117/12.2513566

13 March 2019 Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system

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

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500W (2019) https://doi.org/10.1117/12.2513566
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

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, and Chuan Zhou "Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500W (13 March 2019); https://doi.org/10.1117/12.2513566

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available