Prediction of prostate cancer aggressiveness using (18)F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for (18)F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADC(k), K), mono- (ADC(m)), and biexponential functions (f, D(p), D(f)) while Logan plots were used to calculate volume of distribution (V(T)). In total, 16 unique PET (V(T), SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for V(T) was 0.85. (T)he best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and (18)F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of (18)F-FACBC PET derived parameters (V(T), SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.