Mia A. Levy

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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)
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)
Oncologists managing cancer patients use radiology imaging studies to evaluate changes in measurable cancer lesions. Currently, the textual radiology report summarizes the findings, but is disconnected from the primary image data. This makes it difficult for the physician to obtain a visual overview of the location and behavior of the disease. LesionViewer(More)
Many cancer clinical trials now specify the particular status of a genetic lesion in a patient's tumor in the inclusion or exclusion criteria for trial enrollment. To facilitate search and identification of gene-associated clinical trials by potential participants and clinicians, it is important to develop automated methods to identify genetic information(More)
OBJECTIVE Wide-scale adoption of electronic medical records (EMRs) has created an unprecedented opportunity for the implementation of Rapid Learning Systems (RLSs) that leverage primary clinical data for real-time decision support. In cancer, where large variations among patient features leave gaps in traditional forms of medical evidence, the potential(More)
OBJECTIVE To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118(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)
This perspective describes the motivation, development, and implementation of pathway-based content for My Cancer Genome, an online precision medicine knowledge resource describing clinical implications of genetic alterations in cancer. As researchers uncover more about cancer pathogenesis, we are learning more not only about the specific genes and proteins(More)
BACKGROUND As targeted cancer therapies and molecular profiling become widespread, the era of "precision oncology" is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based(More)
This study tested an innovative model for creating consumer-level content about precision medicine based on health literacy and learning style principles. "Knowledge pearl" videos, incorporating multiple learning modalities, were created to explain genetic and cancer medicine concepts. Cancer patients and caregivers (n=117) were randomized to view(More)