Quantitative image texture analysis predicts malignancy on multiparametric prostate MRI

Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Sonn, Geoffrey (Stanford University School of Medicine) ; Hahn, Lewis (Stanford University School of Medicine) ; Fan, Richard (Stanford University School of Medicine) ; Barker, Jocelyn (Stanford University School of Medicine) ; Rubin, Daniel (Stanford University School of Medicine)

This study demonstrates the feasibility of using quantitative Riesz texture analysis to predict whether a suspicious lesion on mpMRI (PI-RADS ≥3) is cancerous. Particularly notable is the 85% correct prediction for PI-RADS 4 lesions, given the tremendous variability of biopsy results for PI-RADS 4 lesions among different radiologists. Validation of this approach in a larger dataset is ongoing. In the future, we expect that quantitative image analysis will be incorporated into grading systems to refine prostate mpMRI image interpretation, enable greater reproducibility across radiologists, and improve patient counselling and decision making about prostate biopsy.


Conference Type:
short paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Indian Wells, USA, 25-29 October 2015
Date:
Indian Wells, USA
25-29 October 2015
2015
Pagination:
1 p.
Published in
Proceedings of the 91st Annual Meeting of the Western Section of American Urological Association (WSAUA) 2015
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Note: The status of this file is: restricted


 Record created 2015-11-18, last modified 2018-12-20

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