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Proceedings Paper

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI
Author(s): R. Alfano; D. Soetemans; G. S. Bauman; E. Gibson; M. Gaed; M. Moussa; J. A. Gomez; J. L. Chin; S. Pautler; A. D. Ward
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Paper Abstract

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist’s ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057515 (27 February 2018); doi: 10.1117/12.2293341
Show Author Affiliations
R. Alfano, Western Univ. (Canada)
Lawson Health Research Institute (Canada)
D. Soetemans, Western Univ. (Canada)
Lawson Health Research Institute (Canada)
G. S. Bauman, Western Univ. (Canada)
E. Gibson, Univ. College London (United Kingdom)
M. Gaed, Robarts Research Institute (Canada)
M. Moussa, Robarts Research Institute (Canada)
J. A. Gomez, Robarts Research Institute (Canada)
J. L. Chin, Robarts Research Institute (Canada)
S. Pautler, Robarts Research Institute (Canada)
A. D. Ward, Western Univ. (Canada)
Lawson Health Research Institute (Canada)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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