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The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and(More)
OBJECTIVES Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality. METHODS(More)
Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by(More)
Identification of a cohort of patients with specific diseases is an important step for clinical research that is based on electronic health records (EHRs). Informatics approaches combining structured EHR data, such as billing records, with narrative text data have demonstrated utility for such tasks. This paper describes an algorithm combining machine(More)
The scale of tumor genomic profiling is rapidly outpacing human cognitive capacity to make clinical decisions without the aid of tools. New frameworks are needed to help researchers and clinicians process the information emerging from the explosive growth in both the number of tumor genetic variants routinely tested and the respective knowledge to interpret(More)
Objective criteria for measuring response to cancer treatment are critical to clinical research and practice. The National Cancer Institute has developed the Response Evaluation Criteria in Solid Tumors (RECIST) method to quantify treatment response. RECIST evaluates response by assessing a set of measurable target lesions in baseline and follow-up(More)
We are currently in an era of rapidly expanding knowledge about the genetic landscape and architectural blueprints of various cancers. These discoveries have led to a new taxonomy of malignant diseases based upon clinically relevant molecular alterations in addition to histology or tissue of origin. The new molecularly based classification holds the promise(More)
Identifying, tracking and reasoning about tumor lesions is a central task in cancer research and clinical practice that could potentially be automated. However, information about tumor lesions in imaging studies is not easily accessed by machines for automated reasoning. The Annotation and Image Markup (AIM) information model recently developed for the(More)
Increased understanding of intertumoral heterogeneity at the genomic level has led to significant advancements in the treatment of solid tumors. Functional genomic alterations conferring sensitivity to targeted therapies can take many forms, and appropriate methods and tools are needed to detect these alterations. This review provides an update on genetic(More)
As the role of genomics in health care grows, patients increasingly require adequate genetic literacy to fully engage in their care. This study investigated a model for delivering consumer-friendly genetic information to improve understanding of precision medicine using health literacy and learning style principles. My Cancer Genome (MCG), a freely(More)