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94% and 84.65%). An ICER of -$19 131.61 was obtained at the Hospital Un Canto a la Vida and -$1621.85 at PHS. In addition, the cost per quality-adjusted life-year earned was $611.11. Sensitivity analysis showed that the probability of drug use and the relative risk of cardiovascular death associated with such prescription were parameters that most affected the model.

The combination therapy metformin/sitagliptin compared to metformin/glibenclamide was shown not to be cost-effective in the Hospital Un Canto a la Vida, and highly cost-effective in the PHS.

The combination therapy metformin/sitagliptin compared to metformin/glibenclamide was shown not to be cost-effective in the Hospital Un Canto a la Vida, and highly cost-effective in the PHS.Although nanoparticles, polymeric micelles, liposomes, nanoemulsions, and microemulsions were extensively evaluated as formulations for nasal administration of drugs, lyotropic liquid crystal (LLC) mesophases have been few studied. The phase transition from a low-viscosity microemulsion to a more viscous LLC may improve the mucoadhesion of the formulation. PHTPP Estrogen antagonist Donepezil is a drug administered orally in the treatment of Alzheimer's disease, and with gastrointestinal side effects that are typical of acetylcholinesterase inhibitors. Based on this, donepezil administration by nasal pathway using a mucoadhesive LLC may be a feasible alternative. A colloidal formulation was selected from a ternary diagram, combining CETETH-10, oleic acid, and water (404515, w/w). Donepezil was incorporated into the formulation, and the characterisation included in vitro studies, such as mucoadhesion and drug release. Pharmacokinetics in Wistar rats included evaluations by the nasal pathway with donepezil incorporated into microemulsion. A phase transition from an isotropic to an anisotropic system was observed after the swelling of the microemulsion with artificial nasal fluid (12-20 %). The release of donepezil in vitro occurred in a sustained manner. Significant levels of donepezil were achieved in the brain after nasal administration of the microemulsion, as a promising strategy for the treatment of Alzheimer's disease.

To develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN).

Three DCNN architectures working at image-level (DBT slice) were compared two state-of-the-art pre-trained DCNN architectures (AlexNet and VGG19) customized through transfer learning, and one developed from scratch (DBT-DCNN). To evaluate these DCNN-based architectures we analysed their classification performance on two different datasets provided by two hospital radiology departments.DBT slice images were processed following normalization, background correction and data augmentation procedures. The accuracy, sensitivity, and area-under-the-curve (AUC) values were evaluated on both datasets, using receiver operating characteristic curves. A Grad-CAM technique was also implemented providing anindication of the lesion position in the DBT slice.

Accuracy, sensitivity and AUC for the investigated DCNN are in-line with the best performance reported in the field. The DBT-DCNN network developed in this work showed an accuracy and a sensitivity of (90%±4%) and (96%±3%), respectively, with an AUC as good as 0.89±0.04. Ak-fold cross validation test (withk=4) showed an accuracy of 94.0%±0.2%, and a F1-score test provided a value as good as 0.93±0.03. Grad-CAM maps show high activation in correspondence of pixels within the tumour regions.

We developed a deep learning-based framework (DBT-DCNN) to classify DBT images from clinical exams. We investigated also apossible application of the Grad-CAM technique to identify the lesion position.

We developed a deep learning-based framework (DBT-DCNN) to classify DBT images from clinical exams. We investigated also apossible application of the Grad-CAM technique to identify the lesion position.

To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs).

The proposed curriculum consists of two levels Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach).

For the Basic section, KSCs were stratified in four subsections (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated int extent - also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.

MET and AXL dysregulation is reported as a bypass mechanism driving tumour progression in non-small cell lung cancer (NSCLC) with acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI). This non-comparative phase I study investigated the combination of gefitinib with S49076, a MET/AXL inhibitor, in advanced EGFR TKI-resistant NSCLC patients with MET and/or AXL dysregulation.

Patients received S49076 at escalating doses of 500 or 600 mg with a fixed dose of 250 mg gefitinib orally once daily in continuous 28day cycles. MET and AXL dysregulation and EGFR/T790M mutation status were centrally assessed in tumour biopsies at screening. Tumour response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST). EGFR TKI resistance mechanisms were analysed by next-generation sequencing. The clonal evolution of tumours was monitored with the analysis of circulating tumour DNA.

Of 92 pre-screened patients, 22 met the molecular inclusion criteria and 14 were included.