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Galactorrhea is a well-known adverse drug reaction (ADR) of numerous antipsychotic drugs (APD) and is often distressing for those affected. Methodological problems in the existing literature make it difficult to determine the prevalence of symptomatic hyperprolactinemia in persons treated with APDs. Consequently, a large sample of patients exposed to APDs is needed for more extensive evaluation. Data on APD utilization and reports of galactorrhea caused by APDs were analyzed using data from an observational pharmacovigilance program in German-speaking countries-Arzneimittelsicherheit in der Psychiatrie (AMSP)-from 1993 to 2015. 320,383 patients (175,884 female inpatients) under surveillance were treated with APDs for schizophrenia and other indications. A total of 170 events of galactorrhea caused by APDs were identified (0.97 cases in 1000 female inpatient admissions). Most cases occurred during the reproductive age with the highest incidence among patients between 16 and 30 years (3.81 cases in 1000 inpatients). The APDs that were most frequently imputed alone for inducing galactorrhea were risperidone (52 cases and 0.19% of all exposed inpatients), amisulpride (30 resp. 0.48%), and olanzapine (13 resp. 0.05%). In three cases, quetiapine had a prominent role as a probable cause for galactorrhea. High dosages of the imputed APDs correlated with higher rates of galactorrhea. Galactorrhea is a severe and underestimated condition in psychopharmacology. While some APDs are more likely to cause galactorrhea, we identified a few unusual cases. This highlights the importance of alertness in clinical practice and of taking a patient's individual situation into consideration.<br /><br /> To automate the segmentation of whole liver parenchyma on multi-echo chemical shift encoded (MECSE) MR examinations using convolutional neural networks (CNNs) to seamlessly quantify precise organ-related imaging biomarkers such as the fat fraction and iron load.<br /><br /> A retrospective multicenter collection of 183 MECSE liver MR examinations was conducted. An encoder-decoder CNN was trained (107 studies) following a 5-fold cross-validation strategy to improve the model performance and ensure lack of overfitting. Proton density fat fraction (PDFF) and R2* were quantified on both manual and CNN segmentation masks. Different metrics were used to evaluate the CNN performance over both unseen internal (46 studies) and external (29 studies) validation datasets to analyze reproducibility.<br /><br /> The internal test showed excellent results for the automatic segmentation with a dice coefficient (DC) of 0.93 ± 0.03 and high correlation between the quantification done with the predicted mask and the manual segmentation (rPDFF = tomatic procedure for the assessment of chronic diffuse liver diseases in clinical practice.<br /><br />• Whole liver parenchyma can be automatically segmented using convolutional neural networks. • Deep learning allows the creation of automatic pipelines for the precise quantification of liver-related imaging biomarkers such as PDFF and R2*. • MR "virtual biopsy" can become a fast and automatic procedure for the assessment of chronic diffuse liver diseases in clinical practice.<br /><br /> Proton density fat fraction (PDFF) is a validated biomarker of tissue fat quantification. However, validation has been limited to single-center or multi-center series using non-FDA-approved software. Thus, we assess the bias, linearity, and long-term reproducibility of PDFF obtained using commercial PDFF packages from several vendors.<br /><br /> Over 35 months, 438 subjects and 16 volunteers from a multi-center observational trial underwent PDFF MRI measurements using a 3-T MR system from one of three different vendors or a 1.5-T system from one vendor. Fat-water phantom sets were measured as part of each subject's examination. Manual region-of-interest measurements on the %fat image, then cross-sectional bias, linearity, and long-term reproducibility were assessed.<br /><br /> Three hundred ninety-two phantom measurements were evaluable (90%). Bias ranged from 2.4 to - 3.8% for the lowest to the highest weight %fat. Regression fits of PDFF against synthesis weight %fat showed negligible non-linear effects and a linear slope in % fat can be considered true differences when making longitudinal PDFF measurements on different MR systems.<br /><br /> To investigate whether 2-[<br /><br /> F]fluoro-2-deoxy-D-glucose ([<br /><br /> F]FDG) positron emission tomography/magnetic resonance imaging (PET/MRI) can improve the diagnostic performance of TNM staging and help in making an accurate decision regarding resectability in patients with recurrent gastric cancer compared to multi-detector computed tomography (MDCT).<br /><br /> Fifty patients with histologically (n = 31) or clinically (n = 19) confirmed recurrent gastric cancer underwent both MDCT and [<br /><br /> F]FDG PET/MRI. Two radiologists independently assessed TNM staging using MDCT with and without [<br /><br /> F]FDG PET/MRI and scored resectability using a 5-point confidence scale. Diagnostic performance as assessed by radiologists was compared using McNemar's test and receiver operating characteristic curve analysis.<br /><br /> Of the 50 patients, pathologic T and N staging was available in seven and six patients, respectively. Diagnostic accuracies for T and N staging were not significantly different between MDCT with and without [<br /><br /> F]FDG PET/MRI for bothical clues for management options for recurrent gastric cancers.<br /><br />• [18F]FDG PET/MRI can improve diagnostic accuracy for preoperative M staging in patients with recurrent gastric cancers. • [18F]FDG PET/MRI can improve diagnostic accuracy for determining resectability in patients with recurrent gastric cancers. • [18F]FDG PET/MRI can provide critical clues for management options for recurrent gastric cancers.<br /><br /> To evaluate the ability of iodine uptake parameters from hepatic multiphasic CT to predict liver fibrosis, and compare absolute contrast enhancement (ΔHU) with dual-energy iodine density (ID) methods.<br /><br /> One hundred seventeen patients with pathologically proven liver fibrosis who underwent dual-energy CT during portal-venous phase (PVP) and 3-min delayed phase (DP) between January 2017 and Octotber2019 were retrospectively included. Two radiologists measured the hepatic and blood-pool iodine uptake using ΔHU and ID methods; extracellular volume fraction (ECV) and the iodine washout rate (IWR) calculated with both methods were compared between different fibrosis stages (F0-1 vs. F2-4, F0-2 vs. [https://www.selleckchem.com/products/ABT-888.html Veliparib] F3-4, or F0-3 vs. F4). The inter-observer reproducibility (intraclass correlation coefficients [ICCs]) for ECV and IWR was compared between the ΔHU and ID methods. The areas under the receiver operating characteristic curves (AUCs) to predict liver fibrosis severity were calculated for serum and imaging fibrosis markers.