Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images Journal Article


Authors: Fehr, D.; Veeraraghavan, H.; Wibmer, A.; Gondo, T.; Matsumoto, K.; Vargas, H. A.; Sala, E.; Hricak, H.; Deasy, J. O.
Article Title: Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
Abstract: Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93% accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92% for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92% accuracy for cancers occurring in both the PZ and TZ and with 93% for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58% for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63% for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59% for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60% for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.
Keywords: multiparametric mri; gleason score classification; learning from unbalanced data; pca gleason (3+4) vs. (4+3) cancers; pca gleason 6 vs. ≥7
Journal Title: Proceedings of the National Academy of Sciences of the United States of America
Volume: 112
Issue: 46
ISSN: 0027-8424
Publisher: National Academy of Sciences  
Date Published: 2015-11-17
Start Page: E6265
End Page: E6273
Language: English
DOI: 10.1073/pnas.1505935112
PROVIDER: scopus
PMCID: PMC4655555
PUBMED: 26578786
DOI/URL:
Notes: Article -- Export Date: 2 June 2016 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Evis Sala
    95 Sala
  2. Hedvig Hricak
    330 Hricak
  3. Joseph Owen Deasy
    278 Deasy
  4. Tatsuo Gondo
    8 Gondo
  5. Andreas Georg Wibmer
    18 Wibmer
  6. Duc Alexandre Fehr
    6 Fehr