Charles Mayo

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My research interests are focused improving how we care for our patients by developing analytics tools that automate providing quantitative and statistical measures to augment qualitative and anecdotal evaluation. This requires technical efforts, to create databases and software, and clinical efforts, to integrate data aggregation, analysis and use into routine processes. Construction of knowledge based clinical practice improvement databases and standardizations in nomenclatures and ontologies needed to automate aggregation for all patients in a practice and enable data exchanges within and among institutions are facets of this work. A recent example includes, design implementation and use of an electronic prescription database to improve per patient treatment plan evaluation and enable longitudinal monitoring of results of practice quality improvement efforts.  We are also leading a group, sponsored by our professional societies, to define national standards for naming used in data exchanges for clinical trials. Another facet is improvement of patient treatment plan evaluation. Traditionally qualitative, visual inspection of spatial dose relationships to target and normal tissues is used to evaluate plans.  Development of algorithms to calculate vectorized dose volume histograms and other vector based spatial-dose objects provide a means to quantify those evaluations. Recently use of databases of dose information have enabled construction of statistical metrics to improve treatment plan evaluation and development of models for quantifying relationships to outcomes.

Data science applicationsdata driven clinical practice improvement, multi-institutional analysis of factors affecting patient outcomes and practice characterization, nomenclature and ontology.

Cancer Center, April Harris

Jeremy M G Taylor

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I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.


Issam El Naqa

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Our lab’s research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. We operate at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease. These research interests are divided into the following themes:

  • Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (-omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
  • Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
  • Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.
Machine Learning in Radiation Oncology: Theory and Applications

Machine Learning in Radiation Oncology: Theory and Applications