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Pari Pandharipande, MD, MPH

Senior Scientist
Assistant Radiologist in the MGH Division of Abdominal Imaging and Interventional Radiology
Assistant Professor of Radiology, Harvard Medical School
Pari Pandharipande

Pari is a graduate of Cornell University (1994), Cornell University Medical College (1998), and the Harvard School of Public Health (2005). She completed her internship in internal medicine at the New York Presbyterian Hospital-Cornell (1999) and her residency in diagnostic radiology at NYU Medical Center (2004).

Dr. Pandharipande's career goal is to improve cancer outcomes by defining the best roles for imaging-based technologies in the care of cancer patients. Minimally invasive diagnostic and therapeutic approaches are at the forefront of innovations in cancer care. With improvements in both the ability to predict tumor behavior and the range of treatment options available, there is an unparalleled need to re-address treatment paradigms to better match tumor biology and treatment aggressiveness. Dr. Pandharipande's methodologic approach is centered in decision science and regression analysis, but encompasses a broad range of outcomes methods that match the breadth of her research interests.

Dr. Pandharipande's primary project aims to evaluate the cost-effectiveness of a minimally invasive therapy, radiofrequency ablation (RFA), for the treatment of small, incidentally detected renal tumors. This research represents one of the first comprehensive decision analysis and disease modeling efforts in renal tumor therapy to date.

Publications:
  • The Federal Government's Oversight of CT Safety: Regulatory Possibilities.
  • Computer disease simulation models: integrating evidence for health policy.
  • Renal mass biopsy to guide treatment decisions for small incidental renal tumors: a cost-effectiveness analysis.
  • Pregnant patients suspected of having acute appendicitis: effect of MR imaging on negative laparotomy rate and appendiceal perforation rate
  • Lymphotropic nanoparticle-enhanced MRI for independent prediction of lymph node malignancy: a logistic regression model
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Projects:
  • Optimizing Imaging Strategies for Lymph Node Staging
  • Cost-effectiveness of Radiofrequency Ablation for Incidental Renal Tumors
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