Background: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen.Purpose: This study compared different deep learning methods for whole-gland and zonal prostate segmentation. Study Type: Retrospective. Population: A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset. Field strength/Sequence: A 3 T, TSE T 2 -weighted. Assessment: Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two. Statistical Tests: Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance. Results: The best DSC (P < 0.05) in the test set was achieved by ENet: 91% AE 4% for the whole gland, 87% AE 5% for the TZ, and 71% AE 8% for the PZ. U-net and ERFNet obtained, respectively, 88% AE 6% and 87% AE 6% for the whole gland, 86% AE 7% and 84% AE 7% for the TZ, and 70% AE 8% and 65 AE 8% for the PZ. Training and inference time were lowest for ENet. Data Conclusion: Deep learning networks can accurately segment the prostate using T 2 -weighted images. Evidence Level: 4 Technical Efficacy: Stage 2

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The main objective of this clinical trial was to evaluate the effects of magnesium pidolate (15 mmol/day) on blood pressure at rest and during sympathetic stimulation induced by cold, isometric and tilt test; peripheral blood flow has been evaluated by strain-gauge plethysmography. Fourteen mild to moderate hypertensives (8 males, 6 females, age range 40-60 years) were randomly given magnesium or placebo in a double-blind parallel clinical trial for 6 months. In the actively treated group magnesium urinary excretion increased from 5.3 ± 2 to 7.7 ± 2 mmol/24h, and serum magnesium changed from 0.9 ± 0.1 to 1.0 ± 0.2 mmol/l. On magnesium, BP changed at rest from 156/97 ± 12/4 to 149/90 ± 8/3 mm Hg, during cold pressor test from 169/105 ± 9/6 to 174/105 ± 15/4, during isometric exercise from 170/107 ± 13/9 to 170/105 ± 20/6, and during tilt test from 149/96 ± 11/6 to 153/96 ± 17/7 mm Hg. Similar changes were observed in the placebo group. Peripheral resistances were 14.7 ± 4 and 9.8 ± 2 PRU before and after magnesium, respectively. These data indicate that long-term magnesium pidolate supplementation does not affect blood pressure at rest and during sympathetic stimulation, despite a slight, nonsignificant reduction in forearm peripheral resistance.

Gestational choriocarcinoma is a malignant trophoblastic tumor arising from any gestational event, even with a long latency period, generally in the reproductive female. It is associated with a high level of beta-human chorionic gonadotropin. Its primary site is usually the uterus but not all patients have a detectable lesion in this site. Regression of the primary tumor after it has metastasized is not uncommon, and one-third of cases manifest as complications of metastatic disease. In this report we present an uncommon case of gestational choriocarcinoma with lung, liver and jejunal metastases at the time of diagnosis without evidence of pelvic disease, in 34-year-old woman. The main points of interest of our case were the development of the ovarian hyperstimulation syndrome with massive multicystic ovarian enlargement induced by high level of beta-human chorionic gonadotropin and the bleeding of jejunal and liver metastases, due to the high vascularity of the tumor tissue, a condition known as “Choriocarcinoma Syndrome”. We will focus on the radiological findings of metastases, bleeding complications and ovarian hyperstimulation syndrome.

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.