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Proceedings Paper

A study of T[sub]2[/sub]-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer
Author(s): Yahui Peng; Yulei Jiang; Tatjana Antic; Maryellen L. Giger; Scott Eggener; Aytekin Oto
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Paper Abstract

The purpose of this study was to study T2-weighted magnetic resonance (MR) image texture features and diffusionweighted (DW) MR image features in distinguishing prostate cancer (PCa) from normal tissue. We collected two image datasets: 23 PCa patients (25 PCa and 23 normal tissue regions of interest [ROIs]) imaged with Philips MR scanners, and 30 PCa patients (41 PCa and 26 normal tissue ROIs) imaged with GE MR scanners. A radiologist drew ROIs manually via consensus histology-MR correlation conference with a pathologist. A number of T2-weighted texture features and apparent diffusion coefficient (ADC) features were investigated, and linear discriminant analysis (LDA) was used to combine select strong image features. Area under the receiver operating characteristic (ROC) curve (AUC) was used to characterize feature effectiveness in distinguishing PCa from normal tissue ROIs. Of the features studied, ADC 10th percentile, ADC average, and T2-weighted sum average yielded AUC values (±standard error) of 0.95±0.03, 0.94±0.03, and 0.85±0.05 on the Phillips images, and 0.91±0.04, 0.89±0.04, and 0.70±0.06 on the GE images, respectively. The three-feature combination yielded AUC values of 0.94±0.03 and 0.89±0.04 on the Phillips and GE images, respectively. ADC 10th percentile, ADC average, and T2-weighted sum average, are effective in distinguishing PCa from normal tissue, and appear robust in images acquired from Phillips and GE MR scanners.

Paper Details

Date Published: 26 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701H (26 February 2013); doi: 10.1117/12.2007979
Show Author Affiliations
Yahui Peng, The Univ. of Chicago (United States)
Yulei Jiang, The Univ. of Chicago (United States)
Tatjana Antic, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Scott Eggener, The Univ. of Chicago (United States)
Aytekin Oto, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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