MR accurately diagnoses deep infiltrating endometriosis; 3DUS accurately diagnoses deep infiltrating endometriosis in specific locations.

0 3 1What ' s known on the subject? and What does the study add? Preoperative imaging of nodal status in patients undergoing radical cystectomy for bladder cancer lacks diagnostic accuracy. This is the fi rst study that has investigated nodal metastates in bladder cancer using DW-MRI. ADC derived from DW-MRI may be used to differentiate metastatic from non-metastatic lymph nodes. OBJECTIVE• To evaluate whether DW-MRI improves the detection of pelvic lymph nodes metastates in patients with bladder cancer undergoing radical cystectomy. PATIENTS AND METHODS• 36 patients with CT scan negative for nodal metastates underwent DW-MRI before surgery. Diagnostic accuracy of DW-MRI was compared with histopathological fi ndings.Study Type -Diagnostic (exploratory cohort) Level of Evidence 3a

SIRIO proved to be a reliable and effective tool when performing CT-guided PLBs and was especially useful for sampling small ([Formula: see text]20 mm) lesions.

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Arteriovenous malformation (AVM) of the pancreas is a rare condition. Most patients are asymptomatic or alternatively may present with a wide spectrum of symptoms. Traditionally, surgery has been considered the treatment of choice; however, alternative approaches, such as transcatheter embolization (TAE), may be proposed. We report a case of a 48-year-old man with a pancreatic head AVM, presenting with upper abdominal pain and slight anemia. The patient refused surgery and underwent TAE by means of ethylene-vinyl alcohol copolymer (EVOH). At 3 months follow-up, the patient was able to eat regularly, with no residual pain and no signs of anemia.

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Background: axillary lymph node (LN) status is one of the main breast cancer prognostic factors and it is currently defined by invasive procedures. The aim of this study is to predict LN metastasis combining MRI radiomics features with primary breast tumor histological features and patients’ clinical data. Methods: 99 lesions on pre-treatment contrasted 3T-MRI (DCE). All patients had a histologically proven invasive breast cancer and defined LN status. Patients’ clinical data and tumor histological analysis were previously collected. For each tumor lesion, a semi-automatic segmentation was performed, using the second phase of DCE-MRI. Each segmentation was optimized using a convex-hull algorithm. In addition to the 14 semantics features and a feature ROI volume/convex-hull volume, 242 other quantitative features were extracted. A wrapper selection method selected the 15 most prognostic features (14 quantitative, 1 semantic), used to train the final learning model. The classifier used was the Random Forest. Results: the AUC-classifier was 0.856 (label = positive or negative). The contribution of each feature group was lower performance than the full signature. Conclusions: the combination of patient clinical, histological and radiomics features of primary breast cancer can accurately predict LN status in a non-invasive way.