Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

The CAIPRINHA-VIBE with an acceleration factor of R = 2 × 2 is feasible in a clinical setting and is characterized by fast and robust imaging with an image quality comparable with a 2-fold acceleration with GRAPPA.

BackgroundImplementation of DWI in the abdomen is challenging due to artifacts, particularly those arising from differences in tissue susceptibility. Two-dimensional, spatially-selective radiofrequency (RF) excitation pulses for single-shot echo-planar imaging (EPI) combined with a reduction in the FOV in the phase-encoding direction (i.e. zooming) leads to a decreased number of k-space acquisition lines, significantly shortening the EPI echo train and potentially susceptibility artifacts.PurposeTo assess the feasibility and image quality of a zoomed diffusion-weighted EPI (z-EPI) sequence in MR imaging of the pancreas. The approach is compared to conventional single-shot EPI (c-EPI).Material and Methods23 patients who had undergone an MRI study of the abdomen were included in this retrospective study. Examinations were performed on a 3T whole-body MR system (Magnetom Skyra, Siemens) equipped with a two-channel fully dynamic parallel transmit array (TimTX TrueShape, Siemens). The acquired sequences consisted of a conventional EPI DWI of the abdomen and a zoomed EPI DWI of the pancreas. For z-EPI, the standard sinc excitation was replaced with a two-dimensional spatially-selective RF pulse using an echo-planar transmit trajectory. Images were evaluated with regard to image blur, respiratory motion artifacts, diagnostic confidence, delineation of the pancreas, and overall scan preference. Additionally ADC values of the pancreatic head, body, and tail were calculated and compared between sequences.ResultsThe pancreas was better delineated in every case (23/23) with z-EPI versus c-EPI. In every case (23/23), both readers preferred z-EPI overall to c-EPI. With z-EPI there was statistically significantly less image blur (p<0.0001) and respiratory motion artifact compared to c-EPI (p<0.0001). Diagnostic confidence was statistically significantly better with z-EPI (p<0.0001). No statistically significant differences in calculated ADC values were observed between the two sequences.ConclusionZoomed diffusion-weighted EPI leads to substantial image quality improvements with reduction of susceptibility artifacts in pancreatic DWI.