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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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.

Dual-energy computed tomography (DECT) represents an emerging imaging technique which consists of the acquisition of two separate datasets utilizing two different X-ray spectra energies. Several cardiac DECT applications have been assessed, such as virtual monoenergetic images, virtual non-contrast reconstructions, and iodine myocardial perfusion maps, which are demonstrated to improve diagnostic accuracy and image quality while reducing both radiation and contrast media administration. This review will summarize the technical basis of DECT and review the principal cardiac applications currently adopted in clinical practice, exploring possible future applications.