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Palmitoylethanolamide (PEA) is an endogenous lipid mediator known to reduce pain and inflammation. However, only limited clinical studies have evaluated the effects of PEA in neuroinflammatory and neurodegenerative diseases. Multiple sclerosis (MS) is a chronic autoimmune and inflammatory disease of the central nervous system. Although subcutaneous administration of interferon (IFN)-β1a is approved as first-line therapy for the treatment of relapsing-remitting MS (RR-MS), its commonly reported adverse events (AEs) such as pain, myalgia, and erythema at the injection site, deeply affect the quality of life (QoL) of patients with MS. In this randomized, double-blind, placebocontrolled study, we tested the effect of ultramicronized PEA (um-PEA) added to IFN-β1a in the treatment of clinically defined RR-MS. The primary objectives were to estimate whether, with um-PEA treatment, patients with MS perceived an improvement in pain and a decrease of the erythema width at the IFN-β1a injection site in addition to an improvement in their QoL. The secondary objectives were to evaluate the effects of um-PEA on circulating interferon-γ, tumor necrosis factor-α, and interleukin-17 serum levels, N-acylethanolamine plasma levels, Expanded Disability Status Scale (EDSS) progression, and safety and tolerability after 1 year of treatment. Patients with MS receiving um-PEA perceived an improvement in pain sensation without a reduction of the erythema at the injection site. A significant improvement in QoL was observed. No significant difference was reported in EDSS score, and um-PEA was well tolerated. We found a significant increase of palmitoylethanolamide, anandamide and oleoylethanolamide plasma levels, and a significant reduction of interferon-γ, tumor necrosis factor-α, and interleukin-17 serum profile compared with the placebo group. Our results suggest that um-PEA may be considered as an appropriate add-on therapy for the treatment of IFN-β1a-related adverse effects in RR-MS.

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Background Open abdomen (OA) permits the application of damage control surgery principles when abdominal trauma, sepsis, severe acute peritonitis and abdominal compartmental syndrome (ACS) occur. Methods Non-traumatic patients treated with OA between January 2010 and December 2015 were identified in a prospective database, and the data collected were retrospectively reviewed. Patients' records were collected from charts and the surgical and intensive care unit (ICU) registries. The Acosta ''modified'' technique was used to achieve fascial closure in vacuum-assisted wound closure and mesh-mediated fascial traction (VAWCM) patients. Sex, age, simplified acute physiology score II (SAPS II), abdominal compartmental syndrome (ACS), cardiovascular disease (CVD) and surgical technique performed were evaluated in a multivariate analysis for mortality and fascial closure prediction. Results Ninety-six patients with a median age of 69 (40-78) years were included in the study. Sixty-nine patients (72%) underwent VAWCM. Forty-one patients (68%) achieved primary fascia closure: two patients (5%) were treated with VAWC (37 median days) versus 39 patients (95%) who were treated with VAWCM (10 median days) (p = 0.0003). Forty-eight patients underwent OA treatment due to ACS, and 24 patients (50%) survived compared to 36 patients (75%) from the ''other reasons'' group (p = 0.01). The ACS group required longer mechanical ventilator support (p = 0.006), length of stay in hospital (p = 0.005) and in ICU (p = 0.04) and had higher SAPS II scores (p = 0.0002). Conclusions The survival rate was 62%. ACS (p = 0.01), SAPS II (p = 0.004), sex (p = 0.01), pre-existing CVD (p = 0.0007) and surgical technique (VAWC vs VAWCM) (p = 0.0009) were determined to be predictors of mortality. Primary fascial closure was obtained in 68% of cases. VAWCM was found to grant higher survival and primary fascial closure rate.

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

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel imagederived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.

Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.