Speaker Profile
Ritu R. Gill

Ritu R. Gill MD, MPH

Radiology
Boston, Massachusetts, United States of America

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Dr. Gill is a clinical radiologist with sub-specialty training in Cardiothoracic and Oncoradiology and a Masters in Public Health (Clinical Effectiveness). Her key interest is using her clinical skills to build a prognostic model to stratify survival in Chest malignancies. She is currently working on the role of image-guided core biopsies in generating tissue for gene mutation analyses, and for selecting and following patients receiving anti-angiogenic therapies. (GERRAF 2010 project Genomic Profiling Of NSCLC For Personalized Targeted Therapy Using CT-Guided Fine Needle Biopsy: Efficacy, Safety, And Economic Implications’). She is currently working on a Markov model to assess cost-effectiveness of personalized therapy using genomic profiling for Lung Cancer.

She is also affiliated with the International Mesothelioma Program (IMP), founded at Brigham and Women's Hospital/Harvard Medical School by Dr David J. Sugarbaker, MD. The IMP involves basic, translational, and clinical research scientists collaborating to understand the causative factors in the development of MPM and working to translate these findings into improved therapies. As part of this team, they have developed a predictive model to select patients for treatment. Her recently published paper in AJR (Diffusion-weighted MRI of malignant pleural mesothelioma: preliminary assessment of apparent diffusion coefficient in histologic subtypes. AJR) is a step towards developing an imaging biomarker to produce effective treatment strategies. She believes dynamic contrast-enhanced MRI can be used to assess perfusion and tumor vascularity as well as map the heterogeneity of microcirculation in MPM, thus serving as a prognostic biomarker enabling prediction of outcomes, including therapeutic response.

She has an avid interest in 3D and 4D imaging in the chest. She has developed and refined the reconstruction protocols and have improvised the CT techniques on the 320 detector CT scan to create 4D data sets to depict pathology.

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