Machine Learning and Glioma Imaging BiomarkersBackground: Increased computational processing power and advances in database curation will facilitate the development of biomarkers that may contribute to the defeat of cancer in the mid-21 stcentury.Aim: We review how machine learning is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis and treatment response monitoring.Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high grade glioma biomarkers for treatment response monitoring, prognosis and prediction.Results: Magnetic resonance imaging is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, machine learning is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, machine learning also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade and prognosis using magnetic resonance images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of machine learning in glioma imaging).Conclusion: Whilst pioneering, most of the evidence is of a low level having been obtained retrospectively and in single centres. Studies applying machine learning to build neuro-oncology monitoring biomarker models have yet to show overall advantage over those using traditional statistical methods. Development and validation of machine learning models applied to neurooncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, welldocumented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

Background There is convincing evidence that daily whole almond consumption lowers blood LDL cholesterol concentrations, but effects on other cardiometabolic risk factors such as endothelial function and liver fat are still to be determined. Objectives We aimed to investigate whether isoenergetic substitution of whole almonds for control snacks with the macronutrient profile of average snack intakes, had any impact on markers of cardiometabolic health in adults aged 30–70 y at above-average risk of cardiovascular disease (CVD). Methods The study was a 6-wk randomized controlled, parallel-arm trial. Following a 2-wk run-in period consuming control snacks (mini-muffins), participants consumed either whole roasted almonds (n = 51) or control snacks (n = 56), providing 20% of daily estimated energy requirements. Endothelial function (flow-mediated dilation), liver fat (MRI/magnetic resonance spectroscopy), and secondary outcomes as markers of cardiometabolic disease risk were assessed at baseline and end point. Results Almonds, compared with control, increased endothelium-dependent vasodilation (mean difference 4.1%-units of measurement; 95% CI: 2.2, 5.9), but there were no differences in liver fat between groups. Plasma LDL cholesterol concentrations decreased in the almond group relative to control (mean difference −0.25 mmol/L; 95% CI: −0.45, −0.04), but there were no group differences in triglycerides, HDL cholesterol, glucose, insulin, insulin resistance, leptin, adiponectin, resistin, liver function enzymes, fetuin-A, body composition, pancreatic fat, intramyocellular lipids, fecal SCFAs, blood pressure, or 24-h heart rate variability. However, the long-phase heart rate variability parameter, very-low-frequency power, was increased during nighttime following the almond treatment compared with control (mean difference 337 ms2; 95% CI: 12, 661), indicating greater parasympathetic regulation. Conclusions Whole almonds consumed as snacks markedly improve endothelial function, in addition to lowering LDL cholesterol, in adults with above-average risk of CVD. This trial was registered at clinicaltrials.gov as NCT02907684.