Background: A multicentre study was conducted to investigate the impact of sarcopenia as an independent predictor of oncological outcome after radical cystectomy for bladder cancer.Methods: In total, 500 patients with available digital computed tomography scans of the abdomen obtained within 90 days before surgery were identified. The lumbar skeletal muscle index was measured using pre‐operative computed tomography. Cancer‐specific survival (CSS) and overall survival (OS) were estimated using Kaplan–Meier curves. Predictors of CSS and OS were analysed by univariable and multivariable Cox regression models.Results: Based on skeletal muscle index, 189 patients (37.8%) were classified as sarcopenic. Patients with sarcopenia were older compared with their counterparts (P = 0.002), but both groups were comparable regarding to gender, comorbidity, tumor, node, metastasis (TNM) stage, and type of urinary diversion (all P > 0.05). In total, 234 (46.8%) patients died, and of these, 145 (29.0%) died because of urothelial carcinoma of the bladder. Sarcopenic patients had significantly worse 5 year OS (38.3% vs. 50.5%; P = 0.002) and 5 year CSS (49.5% vs. 62.3%; P = 0.016) rates compared with patients without sarcopenia. Moreover, sarcopenia was associated independently with both increased all‐cause mortality (hazard ratio, 1.43; 95% confidence interval 1.09–1.87; P = 0.01) and increased cancer‐specific mortality (hazard ratio, 1.42; 95% confidence interval, 1.00–2.02; P = 0.048). Our results are limited by the lack of prospective frailty assessment.Conclusions: Sarcopenia has been shown to be an independent predictor for OS and CSS in a large multicentre study with patients undergoing radical cystectomy for bladder cancer.

To determine and describe the magnetic resonance (MR) imaging-MR cholangiopancreatographic pancreatic and extrapancreatic findings of autoimmune pancreatitis (AIP) and the probability, site, and MR features of recurrent AIP after steroid therapy. This retrospective study was approved by the institutional review board, and the requirement for informed patient consent was waived. The data of 27 patients with AIP were included in the study. All patients had undergone MR imaging with MR cholangiopancreatography before and after steroid treatment and during follow-up (median follow-up period, 45 months). Image analysis included assessment of pancreatic parenchyma enlargement, signal intensity on T1- and T2-weighted MR images, contrast enhancement, and presence of bile duct and/or renal involvement. The probability of AIP recurrence was assessed by using Kaplan-Meier curves and the unadjusted Cox model. At the time of diagnosis, the AIP-affected pancreatic parenchyma showed diffuse enlargement in 14 (52%) of the 27 patients and segmental enlargement in 13 (48%). The pancreatic parenchyma appeared hypointense on T1-weighted images in all 27 (100%) patients, hyperintense on T2-weighted images in 25 (93%), and isointense in two (7%). During the pancreatic phase of the dynamic contrast material-enhanced study, the affected pancreatic parenchyma appeared hypointense in 25 (93%) patients and isointense in two (7%). During the portal venous and delayed phases, the images of 19 (70%) patients showed delayed enhancement. Bile duct involvement was observed in 10 (37%) patients, and renal involvement was observed in two (7%). After steroid treatment, six (22%) patients had recurrent AIP, with a median disease-free interval of 20.6 months. The sites of recurrence were the pancreas and the kidneys in three of the six patients, solely the pancreas in two patients, and the biliary ducts in one patient. MR imaging with MR cholangiopancreatography enables the diagnosis of pancreatic and extrapancreatic AIP and the assessment of changes after steroid therapy.

Gallbladder adenomyomatosis (GA) is a benign alteration of the gallbladder wall that can be found in up to 9% of patients. GA is characterized by a gallbladder wall thickening containing small bile-filled cystic spaces (i.e., the Rokitansky–Aschoff sinuses, RAS). The bile contained in RAS may undergo a progressive concentration process leading to crystal precipitation and calcification development. A correct characterization of GA is fundamental in order to avoid unnecessary cholecystectomies. Ultrasound (US) is the imaging modality of choice for diagnosing GA; the use of high-frequency probes and a precise focal depth adjustment enable correct identification and characterization of GA in the majority of cases. Contrast-enhanced ultrasound (CEUS) can be performed if RAS cannot be clearly identified at baseline US: RAS appear avascular at CEUS, independently from their content. Magnetic resonance imaging (MRI) should be reserved for cases that are unclear on US and CEUS. At MRI, RAS can be identified with extremely high sensitivity, but their signal intensity varies widely according to their content. Positron emission tomography (PET) may be helpful for excluding malignancy in selected cases. Computed tomography (CT) and cholangiography are not routinely indicated in the suspicion of GA. Teaching points 1. Gallbladder adenomyomatosis is a common benign lesion (1–9% of the patients). 2. Identification of Rokitansky–Aschoff sinuses is crucial for diagnosing gallbladder adenomyomatosis. 3. Sonography is the imaging modality of choice for diagnosing gallbladder adenomyomatosis. 4. Intravenous contrast material administration increases ultrasound accuracy in diagnosing gallbladder adenomyomatosis. 5. Magnetic resonance is a problem-solving technique for unclear cases.

BACKGROUND AND PURPOSE: Intracerebral hemorrhage represents a potentially severe complication of revascularization of acute ischemic stroke. The aim of our study was to assess the capability of iodine extravasation quantification on dual-energy CT performed immediately after mechanical thrombectomy to predict hemorrhagic complications. MATERIALS AND METHODS: Because this was a retrospective study, the need for informed consent was waived. Eighty-five consecutive patients who underwent brain dual-energy CT immediately after mechanical thrombectomy for acute ischemic stroke between August 2013 and January 2017 were included. Two radiologists independently evaluated dual-energy CT images for the presence of parenchymal hyperdensity, iodine extravasation, and hemorrhage. Maximum iodine concentration was measured. Follow-up CT examinations performed until patient discharge were reviewed for intracerebral hemorrhage development. The correlation between dual-energy CT parameters and intracerebral hemorrhage development was analyzed by the Mann-Whitney U test and Fisher exact test. Receiver operating characteristic curves were generated for continuous variables. RESULTS: Thirteen of 85 patients (15.3%) developed hemorrhage. On postoperative dual-energy CT, parenchymal hyperdensities and iodine extravasation were present in 100% of the patients who developed intracerebral hemorrhage and in 56.3% of the patients who did not (P = .002 for both). Signs of bleeding were present in 35.7% of the patients who developed intracerebral hemorrhage and in none of the patients who did not (P < .001). Median maximum iodine concentration was 2.63 mg/mL in the patients who developed intracerebral hemorrhage and 1.4 mg/mL in the patients who did not (P < .001). Maximum iodine concentration showed an area under the curve of 0.89 for identifying patients developing intracerebral hemorrhage. CONCLUSIONS: The presence of parenchymal hyperdensity with a maximum iodine concentration of >1.35 mg/mL may identify patients developing intracerebral hemorrhage with 100% sensitivity and 67.6% specificity.

Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.