Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment
This work addresses prostate cancer diagnosis for medical imaging, but it is incremental as it applies existing methods to a specific dataset.
The researchers tackled the problem of detecting aggressive prostate cancer using multiparametric MR images, achieving an area under the ROC curve of 0.73 with a set of 44 discriminative predictors from 1464 features.
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.