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Proceedings Paper

PI-RADS guided discovery radiomics for characterization of prostate lesions with diffusion-weighted MRI
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Paper Abstract

To demonstrate the added predictive value of radiomic features to prostate radiology scoring scheme (PIRADS), a systematic approach is required to determine whether there is indeed latent predictive information of prostate cancer in diffusion-weighted magnetic resonance images (DW-MRI) that cannot be captured by radiologists’ visual interpretations alone. In this work, we propose a PI-RADS guided discovery radiomics solution where a predictive model for prostate cancer is built by discovering radiomic features that capture information on the phenotype of lesions, which is not visible to radiologists when using PI-RADS scoring system. We investigated patients with PI-RADS scores indicating presence or absence of significant prostate cancer separately and ran experiments on patients with DW-MRI followed by targeted biopsy, using first and second order quantitative imaging features. Our experiments on DW-MRI and pathology data of 50 patients show that the proposed approach improves the overall accuracy of prostate cancer diagnosis significantly compared to PI-RADS scores alone.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095042 (13 March 2019); doi: 10.1117/12.2512550
Show Author Affiliations
Farzad Khalvati, Univ. of Toronto (Canada)
Yucheng Zhang, Univ. of Toronto (Canada)
Phuong H. U. Le, Univ. of Toronto (Canada)
Isha Gujrathi, Univ. of Toronto (Canada)
Masoom A. Haider, Univ. of Toronto (Canada)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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