Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
Volume: 112, Issue: 46
Published: Nov 2, 2015
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...
Paper Details
Title
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
Published Date
Nov 2, 2015
Volume
112
Issue
46
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