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Purpose: To compare iodixanol-enhanced multidetector-row computed tomography angiography (MDCTA) with digital subtraction angiography (DSA), perioperative angiography, or surgical findings in the evaluation of the abdominal aorta and its main branches. Material and Methods: 173 patients with known or suspected aortic aneurysms or stenosis/occlusion of the abdominal aorta or its major branches were enrolled. The iso-osmolar contrast medium iodixanol (320 mg Ι/ml) was used, and data were acquired using four-, eight-, or 16-active-detector-row scanners. Reference diagnoses were provided by surgical findings, interventional findings, or DSA. Diagnostic accuracy was estimated with reference to surgery or interventional arteriography or DSA. Image quality was assessed as excellent, good, sufficient, or insufficient, and correlations were made with attenuation values in the aortic lumen. Tolerability of iodixanol was monitored during the injection for discomfort and other adverse events, and for 72 hours after contrast injection. Results: In 132 of 136 evaluable cases, MDCTA diagnosis matched the reference diagnosis, yielding an agreement rate of 97.1% (95% CI 92.6–99.2%). The quality of most MDCTA scans (147/173) was rated as excellent. Overall mean attenuation was 305.7 HU. MDCTA appeared more accurate than DSA for identification of lesion calcification, thrombus, irregularity, and ulceration. Tolerability of iodixanol was good, and no serious adverse events were reported. Conclusion: MDCTA using iodixanol is a promising, noninvasive alternative for evaluating patients with abdominal aortic disease. [ABSTRACT FROM AUTHOR]

Colonic stents are increasingly used to palliate or alleviate large bowel obstruction in patients with colon cancer and other obstructing lesions in whom a definitive surgical procedure is inappropriate. We report on the outcomes of a large group of patients who underwent deployment of a colon stent in a single institution by a single operator.This was a retrospective observational cohort study of all patients undergoing colonic stenting between September 1995 and May 2002. Data collected included nature of pathology, type of stent used, procedure morbidity, patient survival and details of any definitive procedures performed after stenting.One hundred and seven patients were evaluated (58 male) with a median age of 75 years (range 36–99 years). A total of 112 stents were successfully deployed (46 as an emergency). Twelve patients had double stents inserted coaxially and overlapping. In 7 patients the stent could not be safely deployed. Eighty-seven patients had colorectal cancer, 13 patients had an extra-luminal malignancy, 5 had diverticular strictures and in 2 patients the pathology was unknown. At last review (May 2002) 18 patients were alive, 82 patients had died and 7 patients had been lost to follow-up. Of those patients who died, the median survival after stenting alone was 6 weeks (range 4 days– 36 weeks). Ten patients underwent subsequent definitive surgery. Stent complications included, 2 colonic perforations, 3 stent occlusions and 4 stent migrations.Colonic stenting can be used effectively, with acceptable morbidity, to manage patients presenting with large bowel obstruction. In a smaller number of patients colon stents may safely temporize symptoms while definitive surgery is planned. [ABSTRACT FROM AUTHOR]

Aim: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.Materials and Methods: A retrospective analysis was undertaken of 76 patients (46 tPD, 30 psPD) with early enhancing disease following chemoradiotherapy for glioblastoma. Outcome was determined on follow-up until 6 months post-chemoradiotherapy. Models comprised clinical characteristics, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and 307 quantitative imaging features extracted from enhancing disease and perilesional oedema masks on early post-chemoradiotherapy contrast-enhanced T1-weighted imaging, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm. Naive Bayes five-fold cross-validation was used to validate the final model.Results: Top selected features included age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask, three radiomic features from the enhancing disease mask on ADC, and one radiomic feature from the perilesional oedema mask on T2WI. The final model had an area under the receiver operating characteristics curve (AUC) of 0.80, sensitivity 78.2%, specificity 66.7%, and accuracy of 73.7%.Conclusion: Incorporating a machine learning-based approach using quantitative radiomic features from standard-of-care magnetic resonance imaging (MRI), in combination with clinical characteristics and MGMT promoter methylation status has a complementary effect and improves model performance for early prediction of glioblastoma treatment response. [ABSTRACT FROM AUTHOR]