Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications.

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.

Caustic ingestion: CT findings of esophageal injuries and thoracic complications.

Ingestion of caustic substances, whether accidental or for the purpose of suicide, can cause severe lesions of the lips, oral cavity, pharynx, upper gastrointestinal system, and upper airways. In particular, caustic agents could be responsible for severe esophageal injuries resulting in short- and long-term complications. Because of these important clinical implications, timely diagnosis and appropriate management are crucial. In the evaluation of esophageal injuries, thoraco-abdominal computed tomography (CT) is preferable to endoscopy as it avoids the risk of esophageal perforation and allows the evaluation of esophageal injuries as well as of the surrounding tissue. In this review, we report CT findings of esophageal injuries and possible related thoracic complications caused by caustic ingestion.

Prevalence and clinical significance of incidental findings on multiparametric prostate MRI.

PURPOSE: To assess the prevalence and clinical significance of incidental findings (IFs) detected at multiparametric prostate MRI examination. MATERIALS AND METHODS: Multiparametric prostate MRIs of 647 consecutive patients (mean age 67.1 ± 8.0 years) were retrospectively evaluated by two radiologists recording the presence of all extra-prostatic IFs. Findings were classified as related to or not related to genitourinary system and divided into three classes, according to their clinical significance, as follows: group 1, not significant or scarcely significant; group 2, moderately or potentially significant; and group 3, significant. Differences in distribution of IFs between patients ≤ 65 years old and patients > 65 years old were assessed using Pearson's χ2 or Fisher's exact test. Statistical significance level was set at p < 0.05. RESULTS: Incidental findings (n = 461) were present in 341 (52.7%) patients, while 306 (47.3%) patients did not have any extra-prostatic IF. Overall, IFs were significantly more common in patients > 65 years old (n = 225, 57.0%) compared to patients ≤ 65 years old (n = 116, 46.0%, p = 0.007). There were 139 (30.2%) IFs related to genitourinary system and 322 (69.8%) IFs not related to genitourinary system. Group 3 IFs were almost exclusively present in patients > 65 years old (2.8%, p = 0.034) and included 7 (1.1%) bladder carcinomas, 3 (0.5%) testicle tumors, 2 (0.3%) rectal cancers. Twenty-seven (4.2%) of the 647 patients underwent surgical treatment for IFs not directly related to prostate cancer. CONCLUSION: IFs not related to prostate cancer may be frequently encountered on multiparametric prostate MRI, and they are significantly more common in patients > 65 years old.

Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentation Method.

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.

Fitz-Hugh-Curtis Syndrome Secondary to Postpartum Endometritis: Case Report and Literature Review.

We report a case of Fitz-Hugh-Curtis syndrome (FHCS) 1 week after an urgent cesarean section. Although its onset in the postpartum period is poorly reported in the literature, we suggest that FHCS can complicate puerperal endometritis; therefore, it should be consider in differential diagnosis in puerpera with right upper quadrant pain. Again, it can be useful to perform a contrast computed tomography that allows early diagnosis and conservative treatment.

Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging.

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

Radiomics and Prostate MRI: Current Role and Future Applications.

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.

Congenital Cystic Lesions of the Bile Ducts: Imaging-Based Diagnosis.

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.

Role of computed tomography and magnetic resonance imaging in local complications of acute pancreatitis.

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.