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

Farzad Khalvati; Yucheng Zhang; Phuong H. U. Le; Isha Gujrathi; Masoom A. Haider

doi:10.1117/12.2512550

13 March 2019 PI-RADS guided discovery radiomics for characterization of prostate lesions with diffusion-weighted MRI

Farzad Khalvati, Yucheng Zhang, Phuong H. U. Le, Isha Gujrathi, Masoom A. Haider

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095042 (2019) https://doi.org/10.1117/12.2512550
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Farzad Khalvati, Yucheng Zhang, Phuong H. U. Le, Isha Gujrathi, and Masoom A. Haider "PI-RADS guided discovery radiomics for characterization of prostate lesions with diffusion-weighted MRI", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095042 (13 March 2019); https://doi.org/10.1117/12.2512550

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