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The VMAT technique proves to be safe and effective for treating brain metastases via radiosurgery.

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Introduction: controversy remains about the management of gallstone ileus. While some authors propose enterotomy, others defend the one-stage procedure (simultaneously fistula repair). The objective of the present study was to analyze management options and comparative study their results. Material and methods: retrospective and descriptive study with revision of clinical stories of patients with the diagnosis of gallstone ileus between 1987 and 2008. All the following variables were recorded: dates of hospital admission, surgery and discharge, age, sex, pathological antecedents, preoperative or intraoperative diagnosis, treatment, location of the fistula and location of the obstruction. End-result variables were: postoperative complications, mortality, complications during the follow-up and biliary complications. Results: a total of 40 patients were included of 46,648 admissions. Age, comorbidity, and intraoperative diagnosis were related with poorer short-and long-outcomes. The percentage of postoperative complications was similar for groups with and without fistula repair. Mortality was higher in the group with fistula repair (15 vs. 25%). Biliary complications were more frequent in the group without fistula repair (11 vs. 0%). Sex, location of the fistula and location of the obstruction did not be related with the prognosis. Conclusion: one-stage procedure is related with higher mortality rate than enterotomy alone. Nevertheless, fistula repair reduces the number of biliary complications during the follow-up.

Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.