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.
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY;
authors.forename: Duc
authors.initials: D
authors.lastname: Fehr
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; veerarah@mskcc.org.
authors.forename: Harini
authors.identifier: http://orcid.org/0000-0002-4054-5529
authors.initials: H
authors.lastname: Veeraraghavan
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY;
authors.forename: Andreas
authors.initials: A
authors.lastname: Wibmer
authors.affiliation: Department of Urology, Memorial Sloan Kettering Cancer Center, New York, NY.
authors.forename: Tatsuo
authors.initials: T
authors.lastname: Gondo
authors.affiliation: Department of Urology, Memorial Sloan Kettering Cancer Center, New York, NY.
authors.forename: Kazuhiro
authors.initials: K
authors.lastname: Matsumoto
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY;
authors.forename: Herbert Alberto
authors.initials: HA
authors.lastname: Vargas
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY;
authors.forename: Evis
authors.initials: E
authors.lastname: Sala
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY;
authors.forename: Hedvig
authors.initials: H
authors.lastname: Hricak
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY;
authors.forename: Joseph O
authors.initials: JO
authors.lastname: Deasy
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D011859:Radiography
keywords: Gleason score classification; PCa Gleason (3+4) vs. (4+3) cancers; PCa Gleason 6 vs. ≥7; learning from unbalanced data; multiparametric MRI