Ms. Ruyuan Zuo Profile

Ruyuan Zuo

at Case Western Reserve Univ

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Proceedings Article | 16 March 2020 Paper

Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results

Rakesh Shiradkar, Ruyuan Zuo, Amr Mahran, Lee Ponsky, Sree Harsha Tirumani, Anant Madabhushi

Proceedings Volume 11314, 113143G (2020) https://doi.org/10.1117/12.2551248

KEYWORDS: Magnetic resonance imaging, Prostate cancer, Prostate, Machine learning, Feature extraction, Principal component analysis, Imaging systems, Statistical analysis, Cancer, Pathology

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Periprostatic fat composition on T2-weighted (T2w) MRI has been shown to be associated with aggressive prostate cancer and may influence extraprostatic extension (EPE). In this study, we interrogate the periprostatic fat (PPF) region adjacent to cancer lesion on prostate T2w MRI. Patients with pathologic stage ≥ pT3a are considered to experience EPE (EPE+) and those with stage ≤ T2c are without EPE (EPE-) post radical prostatectomy (RP). We use a cohort of N = 45 prostate cancer patients retrospectively acquired from a single institution who underwent 3T multi-parametric MRI prior to RP. Radiomic features including 1st and 2nd order statistics, Haralick, Gabor, CoLlAGe features are extracted from a region of interest (ROI) in the PPF on pre-surgical T2w MRI delineated by an experienced radiologist. Haralick, gradient and CoLlAGe features were observed to be significantly different (p<0.05) in PPF ROIs between EPE+ and EPE- and were significantly over expressed in EPE+ patients compared to EPE- patients, suggesting a higher heterogeneity within the PPF region for EPE+ patients. These features were used to train machine learning classifiers using a 3-fold cross validation approach in conjunction with feature selection methods to predict EPE. The best classification performance was obtained with Support Vector Machine (SVM) classifiers resulting in an AUC = 0.88 (±0.04). On univariable and multivariable analysis, we observed that radiomic classifier predictions resulted in significant separation between EPE+ and EPE- while none of the routinely used clinical parameters including prostate specific antigen (PSA), Gleason Grade Groups (GGG), age, race and prostate imaging reporting and data system (PI-RADS v2) scores showed significant differences. Our results suggest that radiomic features may quantify the underlying heterogeneity in periprostatic fat and predict patients who are likely to experience extraprostatic extension of disease post RP.

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