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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2018.00630

Predicting Gleason Score of Prostate Cancer Patients using Radiomic Analysis

 Ahmad Chaddad1*,  Tamim Niazi1*, Stephan Probst1, Franck Bladou1, Moris Anidjar1 and Boris Bahoric1
  • 1McGill University, Canada

Purpose: Use of quantitative imaging features and encoding the intra-tumoral heterogeneity from multi-parametric magnetic resonance imaging (mpMRI) for the prediction of Gleason score is gaining attention as a non-invasive biomarker for prostate cancer (PCa). This study tested the hypothesis that radiomic features, extracted from mpMRI, could predict the Gleason score pattern of patients with PCa.

Methods: This analysis included T2-weighted (T2-WI) and apparent diffusion coefficient (ADC, computed from diffusion-weighted imaging) scans of 99 PCa patients from The Cancer Imaging Archive (TCIA). A total of 41 radiomic features were calculated from a local tumor sub-volume (i.e., regions of interest) that is determined by a centroid coordinate of PCa volume, grouped based on their Gleason score patterns. Kruskal-Wallis and Spearman’s rank correlation tests were used to identify features related to Gleason score groups. Random forest (RF) classifier model was used to predict Gleason score groups and identify the most important signature among the 41 radiomic features.

Results: Gleason score groups could be discriminated based on zone size percentage, large zone size emphasis and zone size non-uniformity values (p<0.05). These features also showed a significant correlation between radiomic features and Gleason score groups with a correlation value of -0.35, 0.32, 0.42 for the large zone size emphasis, zone size non-uniformity and zone size percentage, respectively (corrected p < 0.05). RF classifier model achieved an average of the area under the curves of the receiver operating characteristic (ROC) of 83.40, 72.71 and 77.35% to predict Gleason score groups (G1) = 6; 6 < (G2) ≤ (3+4) and (G3) ≥ 4+3, respectively.

Conclusion: Our results suggest that the radiomic features can be used as a non-invasive biomarker to predict the Gleason score of the PCa patients.

Keywords: Radiomics analysis, Classfication, Gleason score (GS), prostate 
cancer, mpMRI

Received: 20 Jul 2018; Accepted: 04 Dec 2018.

Edited by:

Roger M. Bourne, University of Sydney, Australia

Reviewed by:

Andre Bongers, University of New South Wales, Australia
Juan A. Hernandez-Tamames, Erasmus Medical Center, Erasmus University Rotterdam, Netherlands  

Copyright: © 2018 Chaddad, Niazi, Probst, Bladou, Anidjar and Bahoric. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Ahmad Chaddad, McGill University, Montreal, Canada,
Prof. Tamim Niazi, McGill University, Montreal, Canada,