Encorafenib and binimetinib followed by radiotherapy for patients with BRAFV600-mutant melanoma and brain metastases (E-BRAIN/GEM1802 phase II study).

Background: Encorafenib plus binimetinib (EB) is a standard-of-care treatment for advanced BRAFV600-mutant melanoma. We assessed the efficacy and safety of encorafenib plus binimetinib in patients with BRAFV600-mutant melanoma and brain metastasis (BM) and explored if radiotherapy improves the duration of response.

Methods: E-BRAIN/GEM1802 was a prospective, multicenter, single-arm, phase II trial that enrolled patients with melanoma BRAFV600-mutant and BM.

Systematic Review of Automated Diuresis Measurement in Critically Ill Patients.

The measurement of urinary flow is a vital medical indicator for critically ill patients in intensive care units. However, there is a clinical need to automate the real-time measurement of diuresis using Internet of Medical Things devices, allowing continuous monitoring of urine flow. A systematic review of scientific literature, patents, and available commercial products was conducted, leading to the conclusion that there is no suitable device to fulfill this need.

Development of a urinometer for automatic measurement of urine flow in catheterized patients.

Urinary flow measurement and colorimetry are vital medical indicators for critically ill patients in intensive care units. However, there is a clinical need for low-cost, continuous urinary flow monitoring devices that can automatically and in real-time measure urine flow. This need led to the development of a non-invasive device that is easy to use and does not require proprietary disposables.

Democratization of deep learning for segmenting cartilage from MRIs of human knees: Application to data from the osteoarthritis initiative.

In this study, we aimed to democratize access to convolutional neural networks (CNN) for segmenting cartilage volumes, generating state-of-the-art results for specialized, real-world applications in hospitals and research. Segmentation of cross-sectional and/or longitudinal magnetic resonance (MR) images of articular cartilage facilitates both clinical management of joint damage/disease and fundamental research. Manual delineation of such images is a time-consuming task susceptible to high intra- and interoperator variability and prone to errors.

Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads.

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance system) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety.

Think Aloud Protocol Applied in Naturalistic Driving for Driving Rules Generation.

Understanding naturalistic driving in complex scenarios is an important step towards autonomous driving, and several approaches have been adopted for modeling driver's behaviors. This paper presents the methodology known as "Think Aloud Protocol" to model driving. This methodology is a data-gathering technique in which drivers are asked to verbalize their thoughts as they are driving which are then recorded, and the ensuing analysis of the audios and videos permits to derive driving rules.

Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Aim: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical software platforms, were also compared.

Materials And Methods: The data used in this research come from the open database of the Framingham Heart Study, which originated in 1948 in Framingham, Massachusetts as a prospective study of risk factors for cardiovascular disease.

Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving.

This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver-vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers.

Fatal gastrointestinal toxicity with ipilimumab after BRAF/MEK inhibitor combination in a melanoma patient achieving pathological complete response.

Approximately 50% of metastatic melanoma patients harbor BRAF mutations. Several treatment options including the combination of BRAF and MEK inhibitors (BRAF/MEKi) and immunotherapy (mainly anti CTLA-4 and anti PD-1 antibodies), have been shown to improve survival in these patients. Although preclinical data support the synergistic effect of both modalities in combination, data confirming the activity and tolerability of these combinations are not yet available in the clinical setting.

A dosimetric evaluation of the Eclipse AAA algorithm and Millennium 120 MLC for cranial intensity-modulated radiosurgery.

The aim of this study is to assess the accuracy of a convolution-based algorithm (anisotropic analytical algorithm [AAA]) implemented in the Eclipse planning system for intensity-modulated radiosurgery (IMRS) planning of small cranial targets by using a 5-mm leaf-width multileaf collimator (MLC). Overall, 24 patient-based IMRS plans for cranial lesions of variable size (0.3 to 15.

Feasibility of an online adaptive replanning method for cranial frameless intensity-modulated radiosurgery.

To introduce an approach for online adaptive replanning (i.e., dose-guided radiosurgery) in frameless stereotactic radiosurgery, when a 6-dimensional (6D) robotic couch is not available in the linear accelerator (linac).

Dose variations in tumor volumes and organs at risk during IMRT for head-and-neck cancer.

Many head-and-neck cancer (HNC) patients treated with radiotherapy suffer significant anatomical changes due to tumor shrinkage or weight loss. The purpose of this study was to assess dose changes over target volumes and organs at risk during intensity-modulated radiotherapy for HNC patients. Sixteen HNC IMRT patients, all requiring bilateral neck irradiation, were enrolled in the study.