SignificancePredicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes.

ImportanceDespite the rapidly declining number of physician-investigators, there is no consistent structure within medical education so far for involving medical students in research.ObjectiveTo conduct an integrated mixed-methods systematic review and meta-analysis of published studies about medical students' participation in research, and to evaluate the evidence in order to guide policy decision-making regarding this issue.Evidence ReviewWe followed the PRISMA statement guidelines during the preparation of this review and meta-analysis. We searched various databases as well as the bibliographies of the included studies between March 2012 and September 2013. We identified all relevant quantitative and qualitative studies assessing the effect of medical student participation in research, without restrictions regarding study design or publication date. Prespecified outcome-specific quality criteria were used to judge the admission of each quantitative outcome into the meta-analysis. Initial screening of titles and abstracts resulted in the retrieval of 256 articles for full-text assessment. Eventually, 79 articles were included in our study, including eight qualitative studies. An integrated approach was used to combine quantitative and qualitative studies into a single synthesis. Once all included studies were identified, a data-driven thematic analysis was performed.Findings and ConclusionsMedical student participation in research is associated with improved short- and long- term scientific productivity, more informed career choices and improved knowledge about-, interest in- and attitudes towards research. Financial worries, gender, having a higher degree (MSc or PhD) before matriculation and perceived competitiveness of the residency of choice are among the factors that affect the engagement of medical students in research and/or their scientific productivity. Intercalated BSc degrees, mandatory graduation theses and curricular research components may help in standardizing research education during medical school.

Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.