Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach

Chuang Wang; Jayaram K. Udupa; Yubing Tong; Jerry Chen; Sriram Venigalla; Dewey Odhner; Thomas J. Guzzo; John Christodouleas; Drew A. Torigian

doi:10.1117/12.2294550

27 February 2018 Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach

Chuang Wang, Jayaram K. Udupa, Yubing Tong, Jerry Chen, Sriram Venigalla, Dewey Odhner, Thomas J. Guzzo, John Christodouleas, Drew A. Torigian

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Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105752D (2018) https://doi.org/10.1117/12.2294550
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 – T stage < T2, with primary tumor locally confined to the bladder, and group 2 – T stage < T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.

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Chuang Wang, Jayaram K. Udupa, Yubing Tong, Jerry Chen, Sriram Venigalla, Dewey Odhner, Thomas J. Guzzo, John Christodouleas, and Drew A. Torigian "Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach ", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752D (27 February 2018); https://doi.org/10.1117/12.2294550

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KEYWORDS

Magnetic resonance imaging

Bladder

Bladder cancer

Feature extraction

Tumors

Binary data

Image processing

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