Work

Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment

Year: 2016

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1612.00408

Authors: Banerjee, Imon, Hahn, Lewis, Sonn, Geoffrey, Fan, Richard, Rubin, Daniel L.

Institutions: Stanford Medicine, Stanford University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute