MP08-13 MR RADIOMICS IN THE RISK STRATIFICATION OF PROSTATE CANCER

Abstract

You have accessJournal of UrologyImaging/Radiology: Uroradiology I1 Apr 2017MP08-13 MR RADIOMICS IN THE RISK STRATIFICATION OF PROSTATE CANCER Frank Chen, Bino Varghese, Darryl Hwang, Steve Cen, Mihir Desai, Suzanne Palmer, Monish Aron, Manju Aron, Inderbir Gill, Gangning Liang, Andre Abreu, Sameer Chopra, Osamu Ukimura, and Vinay Duddalwar Frank ChenFrank Chen More articles by this author , Bino VargheseBino Varghese More articles by this author , Darryl HwangDarryl Hwang More articles by this author , Steve CenSteve Cen More articles by this author , Mihir DesaiMihir Desai More articles by this author , Suzanne PalmerSuzanne Palmer More articles by this author , Monish AronMonish Aron More articles by this author , Manju AronManju Aron More articles by this author , Inderbir GillInderbir Gill More articles by this author , Gangning LiangGangning Liang More articles by this author , Andre AbreuAndre Abreu More articles by this author , Sameer ChopraSameer Chopra More articles by this author , Osamu UkimuraOsamu Ukimura More articles by this author , and Vinay DuddalwarVinay Duddalwar More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.300AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The current paradigm in prostate cancer risk stratification, including DRE, PSA values, and prostate biopsy, has resulted in overdiagnosis and overtreatment. A noninvasive marker is needed to more accurately differentiate between aggressive and indolent disease. This study evaluated multiparametric magnetic resonance imaging (mpMRI)-derived texture metrics as a biomarker for prostate cancer risk stratification. METHODS In this IRB approved, retrospective study, we identified 66 prostate cancer lesions in patients who underwent 3T mpMRI prior to prostate biopsy. Biopsy proven Prostate cancer lesions were divided into high, intermediate, and low risk categories per National Comprehensive Cancer Network guidelines. Lesion regions of interest were manually segmented from apparent diffusion coefficient (ADC) and T2 weighted images (T2WI). Texture analysis was performed using gray-level co-occurrence matrices (GLCM), fast Fourier transfer-based spectral metrics, and ADC and T2 signal intensity. Kruskall Wallis test and analysis of variance were used to determine if there is an association between texture metrics and prostate cancer risk categories. Stepwise logistic regression was used to select the best predictors in discriminating high risk lesions from other lesions. RESULTS Of the spectral metrics, Complexity Index on ADC and T2WI was significantly different (p<0.01) between the risk categories. ADC-derived GLCM metrics variance, contrast, homogeneity, dissimilarity, and difference of average were significantly different (p<0.01) between the risk categories. Of the texture metrics, GLCM Variance on ADC (ADC_Var) and Information Measures of Correlation 1 on T2WI (T2_ICM1) were the best metrics in discriminating high risk lesions from intermediate and low risk lesions and were selected in the final prediction model. Used alone, the areas under the receiver operator curve (AUC) for ADC_Var and T2_IMC1 were 0.77 (95%CI: 0.64-0.9) and 0.71 (95%CI: 0.59-0.82) respectively. The AUC when using both metrics together was 0.83 (95%CI: 0.72-0.94). CONCLUSIONS mpMRI-based texture analysis can differentiate high risk prostate cancer lesions from intermediate and low risk lesions, demonstrating promise as a biomarker for prostate cancer risk stratification. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e96 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Frank Chen More articles by this author Bino Varghese More articles by this author Darryl Hwang More articles by this author Steve Cen More articles by this author Mihir Desai More articles by this author Suzanne Palmer More articles by this author Monish Aron More articles by this author Manju Aron More articles by this author Inderbir Gill More articles by this author Gangning Liang More articles by this author Andre Abreu More articles by this author Sameer Chopra More articles by this author Osamu Ukimura More articles by this author Vinay Duddalwar More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...