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The purpose of this study was to assess whether Immune CompleX Predictive Index (iXip) improves diagnostic accuracy of multiparametric prostate MRI (mpMRI) for clinically significant prostate cancer. This study included 72 patients (mean age: 68±8 years) with suspicion of prostate cancer and available iXip score. mpMRI images were evaluated by two radiologists according to the PI-RADS v2.1. Reference standard was based on fusion biopsy and standard transperineal 12-point biopsy. Diagnostic accuracy of iXip, mpMRI and their combination were calculated. Optimal cutoff of iXip with sensitivity and specificity was identified using the Youden index. Patients with clinically significant prostate cancers had significantly higher iXip values compared to patients without clinically significant prostate cancers (median 0.411 vs 0.273; p=0.026). The AUROC for iXip was 0.795 (95% CI 0.579-1.000, p=0.026). Sensitivity and specificity were 75% and 100% respectively for mpMRI alone, and 100% and 80% respectively for mpMRI combined with iXip > 0.375. The combination of mpMRI with a cutoff value of iXip > 0.375 has a very high sensitivity for the diagnosis of prostate cancer and a moderately high specificity.

Purpose The aim of our study was to evaluate the prevalence of early complications after Transcatheter Aortic Valve Implantation (TAVI) and their correlation with the Calcium Score (CS) of the aortic valve, aorta and ilio-femoral arteries derived from pre-procedural computed tomography (CT). Materials and methods We retrospectively reviewed 226 patients (100 males, mean age 79.4 ± 6.7 years) undergoing 64-slice CT for pre-TAVI evaluation from January 2018 to April 2021. The population was divided into CS quartiles. Results Overall, 173 patients underwent TAVI procedure, of whom 61% presented paravalvular leak after the procedure, 28% presented bleeding or vascular complications, 25% presented atrioventricular block, and 8% developed acute kidney injury. The prevalence of paravalvular leak and vascular complications was higher in the upper CS quartiles for aortic valve and ilio-femoral arteries. Conclusions Aortic valve and vascular CS could help to predict post-TAVI early complications.

SARS-CoV-2 epidemics has resulted in an unprecedented global health crisis causing a high number of deaths with pneumonia being the most common manifestation. Chest CT is the best imaging modality to identify pulmonary involvement, but unfortunately there are no pathognomonic features for COVID-19 pneumonia, since many other infectious and non-infectious diseases may cause similar alterations. The adoption of artificial intelligence in biomedical imaging has the potential to revolutionize the identification, management, and the patient’s outcome. If adequately validated, it could be used as a support with predictive and prognostic purposes in symptomatic patients but also as a screening test in asymptomatic patients in COVID-19 epidemics. Some studies have already shown the potential adoption of artificial intelligence for detection of COVID-19 infection, or even to differentiate from community-acquired pneumonia, but at present artificial intelligence cannot routinely applied for COVID-19 due to several limitations. This book chapter will first revise the basics of radiomics with a short practical and easy guide for radiologists; then, the main radiological findings of COVID-19 pneumonia will be presented with the most relevant information that are assessed to evaluate extent of the disease; finally, the main current literature on potential clinical application of radiomics and artificial intelligence for COVID-19 will be presented together with limitations and perspectives.

Purpose: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation. Materials and Methods: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate volumes obtained with ellipsoid formula, manual segmentation, and automated segmentation. To provide an evaluation of the similarity or difference to manual segmentation, sensitivity, positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric difference (VD) were calculated. Results: Differences between prostate volume obtained from ellipsoid formula versus manual segmentation and versus automatic segmentation were statistically significant (P < 0.049318 and P < 0.034305, respectively), while no statistical difference was found between volume obtained from manual versus automatic segmentation (P = 0.438045). The performance of ENet versus manual segmentations was good providing a sensitivity of 93.51%, a PPV of 87.93%, a DSC of 90.38%, a VOE of 17.32% and a VD of 6.85%. Conclusion: The presence of median lobe enlargement may lead to MRI volume overestimation when using the ellipsoid formula so that a segmentation method is recommended. ENet volume estimation showed great accuracy in evaluation of prostate volume similar to that of manual segmentation.

Congenital cystic lesions of the bile ducts represent a spectrum of liver and biliary system lesions, resulting from abnormal embryologic development of the ductal plate. These disorders include Caroli disease, choledochal cysts, autosomal dominant polycystic liver disease, congenital hepatic fibrosis, and biliary hamartomas. Each disorder carries a peculiar clinical presentation, prognosis, and risk of complications. Knowledge of radiological findings of fibropolycystic liver diseases is crucial for their appropriate detection and for differential diagnosis with other similar hepatic cystic lesions, in order to avoid relevant misdiagnosis. The aim of this review is to provide an illustrative summary of the most relevant imaging findings of these conditions as encountered on ultrasound, computed tomography, and magnetic resonance imaging, and provide pearls for imaging-based differential diagnosis. (c) 2019 Elsevier Inc. All rights reserved.

Acute pancreatitis (AP) represents a pancreas inflammation of sudden onset that can present different degrees of severity. AP is a frequent cause of acute abdomen and its complications are still a cause of death. Biliary calculosis and alcohol abuse are the most frequent cause of AP. Computed tomography (CT) and magnetic resonance imaging (MRI) are not necessary for the diagnosis of AP but they are fundamental tools for the identification of the cause, degree severity and AP complications. AP severity assessment is in fact one of the most important issue in disease management. Contrast-enhanced CT is preferred in the emergency setting and is considered the gold standard in patients with AP. MRI is comparable to CT for the diagnosis of AP but requires much more time so it is not usually chosen in the emergency scenario. Complications of AP can be distinguished in localized and generalized. Among the localized complications, we can identify: acute peripancreatic fluid collections (APFC), pseudocysts, acute necrotic collections (ANC), walled off pancreatic necrosis (WOPN), venous thrombosis, pseudoaneurysms and haemorrhage. Multiple organ failure syndrome (MOFS) and sepsis are possible generalized complications of AP. In this review, we focus on CT and MRI findings in local complications of AP and when and how to perform CT and MRI. We paid also attention to recent developments in diagnostic classification of AP complications.