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The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-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 (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, 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 VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

PurposeTo develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2).MethodsT2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.ResultsIn total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82-0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.ConclusionTexture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.

PURPOSE To evaluate different fitting methods for intravoxel incoherent motion (IVIM) imaging of prostate cancer in the terms of repeatability and Gleason score prediction. METHODS Eighty-one patients with histologically confirmed prostate cancer underwent two repeated 3 Tesla diffusion-weighted imaging (DWI) examinations performed using 14 b-values in the range of 0-500 s/mm2 and diffusion time of 19.004 ms. Mean signal intensities of regions-of-interest were fitted using five different fitting methods for IVIM as well as monoexponential, kurtosis, and stretched exponential models. The fitting methods and models were evaluated in the terms of fitting quality [Akaike information criteria (AIC)], repeatability, and Gleason score prediction. Tumors were classified into three groups (3 + 3, 3 + 4, > 3 + 4). Machine learning algorithms were used to evaluate the performance of the combined use of the parameters. Simulation studies were performed to evaluate robustness of the fitting methods against noise. RESULTS Monoexponential model was preferred over IVIM based on AIC. The "pseudodiffusion" parameters demonstrated low repeatability and clinical value. Median "pseudodiffusion" fraction values were below 8.00%. Combined use of the parameters did not outperform the monoexponential model. CONCLUSION Monoexponential model demonstrated the highest repeatability and clinical values in the regions-of-interest based analysis of prostate cancer DWI, b-values in the range of 0-500 s/mm2 . Magn Reson Med 77:1249-1264, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Background The molecular mechanisms mediating postnatal loss of cardiac regeneration in mammals are not fully understood. We aimed to provide an integrated resource of mRNA , protein, and metabolite changes in the neonatal heart for identification of metabolism‐related mechanisms associated with cardiac regeneration. Methods and Results Mouse ventricular tissue samples taken on postnatal day 1 (P01), P04, P09, and P23 were analyzed with RNA sequencing and global proteomics and metabolomics. Gene ontology analysis, KEGG pathway analysis, and fuzzy c‐means clustering were used to identify up‐ or downregulated biological processes and metabolic pathways on all 3 levels, and Ingenuity pathway analysis (Qiagen) was used to identify upstream regulators. Differential expression was observed for 8547 mRNA s and for 1199 of 2285 quantified proteins. Furthermore, 151 metabolites with significant changes were identified. Differentially regulated metabolic pathways include branched chain amino acid degradation (upregulated at P23), fatty acid metabolism (upregulated at P04 and P09; downregulated at P23) as well as the HMGCS ( HMG ‐CoA [hydroxymethylglutaryl‐coenzyme A] synthase)–mediated mevalonate pathway and ketogenesis (transiently activated). Pharmacological inhibition of HMGCS in primary neonatal cardiomyocytes reduced the percentage of BrdU‐positive cardiomyocytes, providing evidence that the mevalonate and ketogenesis routes may participate in regulating the cardiomyocyte cell cycle. Conclusions This study is the first systems‐level resource combining data from genomewide transcriptomics with global quantitative proteomics and untargeted metabolomics analyses in the mouse heart throughout the early postnatal period. These integrated data of molecular changes associated with the loss of cardiac regeneration may open up new possibilities for the development of regenerative therapies.

Computer aided diagnosis (CADx) systems for magnetic resonance imaging of prostate have shown potential to increase accuracy for detection of cancer. The purpose of this study is to introduce a method for CADx to detect prostate cancer based on texture features extracted from a grid placed on diffusion weighted imaging (DWI) parametric maps. Texture maps of DWI parametric maps (monoexponential: ADC m , kurtosis: ADC k and K) from 67 patients were obtained. Then the texture maps were divided in cubes, and median texture features were calculated for each cube. The features were used to train prediction models. Area under the curve (AUC) value was used to assess the prediction efficiency. In total, 875 texture features were extracted with Gabor filter, GLCM, LBP, Haar transform, and Hu moments. Statistical features were also calculated. The union of texture features from the ADC m ADC k and K parametric maps demonstrated high performance with AUC values of 0.81 to 0.85.