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Proceedings Paper

Evaluating texture-based prostate cancer classification on multi-parametric magnetic resonance imaging and prostate specific membrane antigen positron emission tomography
Author(s): R. Alfano; G. S. Bauman; J. Thiessen; I. Rachinsky; W. Pavlosky; J. Butler; M. Gaed; M. Moussa; J. A. Gomez; J. L. Chin; S. Pautler; A. D. Ward
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Paper Abstract

In-vivo imaging of the prostate has shown to be useful for prostate cancer (PCa) localization especially during biopsy procedures. Multi-parametric MRI (mp-MRI) is gaining rapid popularity amongst clinicians but is complex and difficult to interpret by even expert radiologists. Prostate specific membrane antigen positron emission tomography (PSMA PET) is emerging as a new tool for PCa detection and has shown promising results towards lesion identification. Both imaging procedures suffer from intra- and inter- observer variability in PCa detection. Computer-aided diagnosis (CAD) systems have been developed as a solution to mitigate observer variability and have shown to boost diagnostic accuracy. There are currently no studies published that assessed the benefit of incorporating PSMA PET imaging and mp-MRI into a CAD system for PCa detection. We compared the accuracy of CAD models trained and tested on features from mp-MRI+PSMA PET, mp-MRI and PSMA PET by training on 1-10 features chosen from three feature selection methods for 10 different classifiers for each of the three experiments. We found that models trained on mp-MRI provided lower overall error and greater specificity, and models trained on mp-MRI+PSMA PET and PSMA PET provided greater sensitivity to lesions in the central gland, which is a known area of difficulty for mp-MRI. Further validation using a larger dataset is required to prove the added benefit of PSMA PET imaging as a second modality to PCa CAD systems. Once fully validated, these results will demonstrate the added benefit of incorporating PSMA PET imaging into CAD models towards PCa detection.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143F (16 March 2020); doi: 10.1117/12.2551077
Show Author Affiliations
R. Alfano, Baines Imaging Research Lab. (Canada)
Western Univ. (Canada)
Lawson Health Research Institute (Canada)
G. S. Bauman, Western Univ. (Canada)
J. Thiessen, Western Univ. (Canada)
Lawson Health Research Institute (Canada)
I. Rachinsky, Lawson Health Research Institute (Canada)
W. Pavlosky, Lawson Health Research Institute (Canada)
J. Butler, Lawson Health Research Institute (Canada)
M. Gaed, Western Univ. (Canada)
M. Moussa, Western Univ. (Canada)
J. A. Gomez, Western Univ. (Canada)
J. L. Chin, Western Univ. (Canada)
S. Pautler, Western Univ. (Canada)
A. D. Ward, Baines Imaging Research Lab. (Canada)
Western Univ. (Canada)
Lawson Health Research Institute (Canada)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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