MP52-02 COMBINING STANDARDIZED UPTAKE VALUE OF 18 F-PSMA PET/CT AND APPARENT DIFFUSION COEFFICIENT OF MRI PREDICTS PATHOLOGICAL UPGRADING FROM TARGETED BIOPSY TO RADICAL PROSTATECTOMY IN LOCALIZED PROSTATE CANCER

You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy II (MP52)1 May 2024MP52-02 COMBINING STANDARDIZED UPTAKE VALUE OF 18F-PSMA PET/CT AND APPARENT DIFFUSION COEFFICIENT OF MRI PREDICTS PATHOLOGICAL UPGRADING FROM TARGETED BIOPSY TO RADICAL PROSTATECTOMY IN LOCALIZED PROSTATE CANCER Wen Liu, Miao Wang, and Ming Liu Wen LiuWen Liu , Miao WangMiao Wang , and Ming LiuMing Liu View All Author Informationhttps://doi.org/10.1097/01.JU.0001008864.84854.b7.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Standardized uptake value (SUV) derived from prostate-specific membrane antigen positron emission tomography (PSMA PET) and apparent diffusion coefficient (ADC) obtained from magnetic resonance imaging (MRI) can provide insights into tissue metabolism and cellularity, respectively. Given their potential complementary information, our study aimed to explore the predictive value of SUV, ADC, and their combination in assessing the likelihood of pathological upgrading from targeted biopsy to radical prostatectomy (RP) at the lesion-based level. METHODS: This retrospective study included 102 patients with localized prostate cancer (PCa) who underwent 18F-PSMA PET/CT and MRI prior to RP. We designated the lesion with the highest ISUP grade group in post-RP pathology as the primary lesion for the lesion-based analysis of pathological upgrading. SUV characteristics (SUVmax, SUVmean, SUVpeak, prostate-specific membrane antigen tumor volume [PSMA-TV], and total lesion of prostate-specific membrane antigen [TL-PSMA]) and ADC values (ADCmin and ADCmean) were extracted from the region of interest in the images. Pathological upgrading was defined as an increase in ISUP grade groups in the targeted lesion. We employed logistic regression, nonlinear analysis, and receiver operating characteristic analysis to assess predictive capabilities. RESULTS: Among the 102 patients, 33 (32.3%) experienced pathological upgrading. Spearman analysis revealed a significant inverse correlation of ADCmin with SUVmean (R = -0.25, p<0.05), SUVmax (R = -0.28, p<0.05), and SUVpeak (R = -0.28, p<0.05). Multivariable logistic regression indicated that SUVmean (OR 1.168, 95% CI 1.060-1.326), SUVmax (OR 1.084, 95% CI 1.032-1.152), SUVpeak (OR 1.298, 95% CI 1.130-1.550), and ADCmin (OR 0.336, 95% CI 0.193-0.529) independently predicted pathological upgrading. Notably, SUVmean/ADCmin (OR 2.630, 95% CI 1.716-4.618), SUVmax/ADCmin (OR 1.743, 95% CI 1.369-2.382), and SUVpeak/ADCmin (OR 5.705, 95% CI 2.782-14.896) demonstrated stronger predictive values. Furthermore, SUVmean/ADCmin (AUC=0.818), SUVmax/ADCmin (AUC=0.805), and SUVmean/ADCmin (AUC=0.813) exhibited higher area under the curve values compared to SUVmean (AUC=0.750), SUVmax (AUC=0.751), SUVpeak (AUC=0.755), and ADCmin (AUC=0.701) for predicting pathological upgrading. CONCLUSIONS: In summary, SUV and ADCmin independently hold predictive value for pathological upgrading in localized PCa. The combined parameter SUV/ADCmin enhances predictive accuracy, suggesting potential clinical utility for risk stratification in PCa patients. Source of Funding: This work was supported by National High Level Hospital Clinical Research Funding [BJ-2022-115], National Key Research and Development Program of China [2022YFC3602900], and National High Level Hospital Clinical Research Funding [BJ-2022-144] © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e851 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Wen Liu More articles by this author Miao Wang More articles by this author Ming Liu More articles by this author Expand All Advertisement PDF downloadLoading ...