PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI) (2017)

You have accessJournal of UrologyProstate Cancer: Markers II1 Apr 2017PD65-08 DISTINGUISHING LOW VERSUS HIGH RISK PROSTATE CANCER LESIONS USING RADIOMIC FEATURES DERIVED FROM MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING (MRI) Rakesh Shiradkar, Soumya Ghose, Robert Villani, Eran Ben-Levi, Ardeshir Rastinehad, and Anant Madabhushi Rakesh ShiradkarRakesh Shiradkar More articles by this author , Soumya GhoseSoumya Ghose More articles by this author , Robert VillaniRobert Villani More articles by this author , Eran Ben-LeviEran Ben-Levi More articles by this author , Ardeshir RastinehadArdeshir Rastinehad More articles by this author , and Anant MadabhushiAnant Madabhushi More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2958AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Multi-parametric magnetic resonance imaging (mp-MRI) based prostate imaging reporting and data system (PIRADS) is limited in confidently and robustly distinguishing clinically significant and insignificant prostate cancer (PCa). Radiomic features employ image processing methods to characterize specific patterns in images and have been shown to better characterize PCa than mp-MRI signal intensities alone. For example, gradient features quantify the appearance of edges, Haralick features distinguish homogenous low intensity (PCa) from normal regions and Gabor features quantify appearance of PCa at multiple orientations and scales. In this study, we aim to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa as defined by the D'Amico criteria. METHODS A retrospective cohort of 452 PCa patients who underwent a 3 Tesla mp-MRI scan was considered for this study. A subset of 72 patients comprising 153 lesions was chosen chronologically based on PIRADS to obtain a statistically balanced cohort. D'Amico criteria were available for 83 lesions and was used to categorize into low (N= 26), intermediate (N = 43) and high (N = 14) risk groups. A balanced dataset of N = 28 lesions with 14 lesions from each of high and low risk categories was finally assembled for radiomic feature analysis. RESULTS A set of 101 radiomic features were extracted on a voxel-wise basis within the lesion region of interest (ROI) from each of T2w and ADC MRI sequences. First order statistics (mean, variance, skewness and kurtosis) were computed within each ROI to obtain 808 features per ROI. Of these, 44 features showed statistically significant differences between high and low risk lesions. Specifically, variance and skewness of T2w gradient and Gabor features, skewness and kurtosis of ADC Haralick and Laws features showed p<0.05 using Wilcoxon Rank-Sum test (representative results are shown in Figure). A random forests classifier trained using these radiomic features within a 3-fold cross validation framework resulted in an AUC of 0.96. CONCLUSIONS Radiomic features derived from mp-MRI distinguish high and low risk prostate cancer lesions as defined by D'Amico criteria. An independent validation of these features is required on a separate test set. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1269 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Rakesh Shiradkar More articles by this author Soumya Ghose More articles by this author Robert Villani More articles by this author Eran Ben-Levi More articles by this author Ardeshir Rastinehad More articles by this author Anant Madabhushi More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...