Ionizing radiation is one of the main risks affecting healthcare workers and patients worldwide. Special attention has to be paid to medical staff in the vicinity of radiological equipment or patients undergoing radioisotope procedures. To measure radiation values, traditional area meters are strategically placed in hospitals and personal dosimeters are worn by workers. However, important drawbacks inherent to these systems in terms of cost, detection precision, real time data processing, flexibility, and so on, have been detected and carefully detailed. To overcome these inconveniences, a low cost, open-source, portable radiation measurement system is proposed. The goal is to deploy devices integrating a commercial Geiger-Muller (GM) detector to capture radiation doses in real time and to wirelessly dispatch them to a remote database where the radiation values are stored. Medical staff will be able to check the accumulated doses first hand, as well as other statistics related to radiation by means of a smartphone application. Finally, the device is certified by an accredited calibration center, to later validate the entire system in a hospital environment.

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.

Whole-brain radiation therapy and stereotactic radiosurgery are two treatment modalities commonly utilized to treat brain metastases (BMs). The aim of this study is to retrospectively analyze the main radio-oncologic and clinical-demographic aspects of a cohort of BM patients treated with Volumetric Modulated Arc Therapy for radiosurgery (VMAT-RS). This is a cross-sectional observational design study with a retrospective review of the medical records of patients with brain metastases treated with VMAT-RS between 2012 and 2018. Clinical and demographic data, with special attention to sex, age, primary tumor, brain tumor-related epilepsy (BTRE), number and brain location of BMs, Karnofsky Performance Status (KPS), the updated DS-GPA prognostic index, and the survival estimated according to the Kaplan–Meier model from the date of radiosurgery, were analyzed. One hundred and twenty-one patients with 229 BMs were treated with VMAT-RS. Patients presented 1–4 BMs, which were treated with five non-coplanar VMAT arcs. Sixty-eight percent of the patients had lung cancer, and 35% of the BMs were in the frontal lobe. The proportion of local control was 88.5%. BTRE prevalence was 30.6%. The median survival time (MST) was 7.7 months. In the multivariate analysis of the Cox regression model, KPS ≥ 70 (HR_{KPS < 70} = 2.59; p = 0.001) and higher DS-GPA (HR_DS-GPAII = 0.55, p = 0.022; HR_DS-GPAIII-IV = 0.38, p = 0.006) were associated with improved survival. In the univariate analysis, primary tumor, age, and the presence of metastases in the posterior fossa (PFBMs) were also significant. In conclusion, the VMAT-RS is a technique with an overall survival rate comparable to other radiosurgery techniques. The median survival is significantly longer for those with higher KPS and DS-GPA. Other variables, such as the type of primary tumor, age, and PFBMs, could also influence survival, although further studies are needed.

Whole-brain radiation therapy and stereotactic radiosurgery are two treatment modalities commonly utilized to treat brain metastases (BMs). The aim of this study is to retrospectively analyze the main radio-oncologic and clinical-demographic aspects of a cohort of BM patients treated with Volumetric Modulated Arc Therapy for radiosurgery (VMAT-RS). This is a cross-sectional observational design study with retrospective review of medical records of patients with brain metastases treated with VMAT- RS between 2012 and 2018. Clinical and demographic data, with special attention to sex, age, primary tumor, brain tumor-related epilepsy (BTRE), number and brain location of BMs, Karnofsky Performance Status (KPS), the updated DS-GPA prognostic index and the survival estimated according to the Kaplan-Meier model from the date of radiosurgery were analyzed. One hundred and twenty-one patients with 229 BMs were treated with VMAT-RS. Patients presented 1-4 BMs, which were treated with 5 non-coplanar VMAT arcs. Sixty-eight percent of the patients had lung cancer and 35% of the BMs were in the frontal lobe. Proportion of local control was 88.5%. BTRE prevalence was 30.6%. The median survival time (MST) was 7.7 months. In the multivariate analysis of the Cox Regression model KPS70 (HRKPS<70=2.59; p=0.001) and higher DS-GPA (HRDS-GPAII =0.55, p=0.022; HRDS-GPAIII-IV =0.38, p=0.006) were associated with improved survival. In the univariate analysis, primary tumor, age and the presence of metastases in the posterior fossa (PFBMs) were also significative. In conclusion, the VMAT-RS is a technique with an overall survival comparable to other radiosurgery techniques. The median survival is significantly longer in those with higher KPS and DS-GPA. Other variables such as the type of primary tumor, age and PFBMs could also influence survival, although further studies are needed.