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

Yahui Peng; Yulei Jiang; Tatjana Antic; Maryellen L. Giger; Scott Eggener; Aytekin Oto

doi:10.1117/12.2007979

26 February 2013 A study of T₂-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

Yahui Peng, Yulei Jiang, Tatjana Antic, Maryellen L. Giger, Scott Eggener, Aytekin Oto

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86701H (2013) https://doi.org/10.1117/12.2007979
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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 10^th 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 10^th 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.

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Yahui Peng, Yulei Jiang, Tatjana Antic, Maryellen L. Giger, Scott Eggener, and Aytekin Oto "A study of T₂-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701H (26 February 2013); https://doi.org/10.1117/12.2007979

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