Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment

TL;DR

This paper presents an automated MRI-based method to distinguish aggressive prostate cancer from non-aggressive cases by analyzing multiple imaging features, achieving moderate classification accuracy.

Contribution

It introduces a comprehensive multiparametric MRI analysis approach with a large feature set to assess prostate cancer aggressiveness automatically.

Findings

44 key features effectively differentiate aggressive from non-aggressive CaP

Achieved an ROC AUC of 0.73 in classification

Validated on a dataset of 79 patients

Abstract

We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC). The proposed methodology was tested on a dataset of 79 patients (40 aggressive, 39 non-aggressive). We evaluated the performance of a wide range of popular quantitative imaging features on the characterization of aggressive versus non-aggressive CaP. We found that a group of 44 discriminative predictors among 1464 quantitative imaging features can be used to produce an area under the ROC curve of 0.73.