Purpose To compare small bowel distension and side effects between a diluted polyethylene glycol (PEG) solution and a low-density (0.1% w/v) barium sulfate suspension (LDBSS) for CT enterography (CTE) preparation. Materials and Methods Total 173 consecutive patients who underwent CTE were enrolled in this study. The LDBSS (1 L) was used in 50 patients, and the diluted iso-osmotic PEG solution (1 L) was used in 123 patients. Two blinded radiologists independently scored jejunal and ileal distensions on a 5-point scale. To compare side effects between the two groups, the patients reported whether they had immediate complications after the administration of the oral contrast media. Results For ileal and jejunal distension, the diluted PEG solution showed no difference from the LDBSS for either reader (ileum: reader 1, median, 4; 4, interquartile range, 3–4; 3–4, p = 0.997; reader 2, median, 4; 4, interquartile range, 3.3–4.0; 3–4, p = 0.064; jejunum: reader 1, median, 2; 2, interquartile range, 2–3; 2–3, p = 0.560; reader 2, median, 3; 2, interquartile range, 2–3; 2–3, p = 0.192). None of the patients complained of immediate complications following administration of either of the oral contrast media. Conclusion The diluted PEG solution showed comparable bowel distension compared to LDBSS and no immediate side effects; thus, it can be a useful alternative.

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Background: We investigated the feasibility of a deep learning algorithm (DLA) based on apparent diffusion coefficient (ADC) maps for the segmentation and discrimination of clinically significant cancer (CSC, Gleason score ≥ 7) from non-CSC in patients with prostate cancer (PCa). Methods: Data from a total of 149 consecutive patients who had undergone 3T-MRI and been pathologically diagnosed with PCa were initially collected. The labelled data (148 images for GS6, 580 images for GS7) were applied for tumor segmentation using a convolutional neural network (CNN). For classification, 93 images for GS6 and 372 images for GS7 were used. For external validation, 22 consecutive patients from five different institutions (25 images for GS6, 70 images for GS7) representing different MR machines were recruited. Results: Regarding segmentation and classification, U-Net and DenseNet were used, respectively. The tumor Dice scores for internal and external validation were 0.822 and 0.7776, respectively. As for classification, the accuracies of internal and external validation were 73 and 75%, respectively. For external validation, diagnostic predictive values for CSC (sensitivity, specificity, positive predictive value and negative predictive value) were 84, 48, 82 and 52%, respectively. Conclusions: Tumor segmentation and discrimination of CSC from non-CSC is feasible using a DLA developed based on ADC maps (b2000) alone.