Dania Daye, MD, PhD is an Assistant Professor of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School. She is the co-director of IR Research at MGH and Director of the Precision Interventional and Medical Imaging (PIMI) Research Group. Her research centers around the applications of machine learning and computer vision for precision medicine.
For her research, Dr. Daye is the recipient of many awards that include the Association of American Physicians Stanley J. Korsmeyer Young Investigator Award, the Association of University Radiologists (AUR) Memorial Award, and the 40 under 40 MedTech Boston Healthcare Innovators. Dr. Daye previously served as the President of the American Physician Scientists Association and currently serves on the board of directors. For her local and national leadership roles, Dr. Daye was the recipient of the American Medical Association (AMA) Foundation Leadership Award.
Dr. Daye is founding co-chair of the Women in Radiology Steering Committee at MGH, founding Director of the New England Women in Radiology Invited Lectureship Program and current chair of the MGH Radiology Diversity, Equity and Inclusion Committee. Dr. Daye is a graduate of the MD/PhD program at the University of Pennsylvania, where she completed her PhD in Bioengineering as an HHMI-NIBIB Interfaces Scholar.
MD, PhD, University of Pennsylvania
1. Ashraf AB, Daye D, Gavenonis S, Mies C, Feldman M, Rosen M, Kontos D. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology. 2014 Aug;272(2):374-84. doi: 10.1148/radiol.14131375. Epub 2014 Apr 4. PMID: 24702725; PMCID: PMC4564060.
2. Daye D, Staziaki PV, Furtado VF, Tabari A, Fintelmann FJ, Frenk NE, Shyn P, Tuncali K, Silverman S, Arellano R, Gee MS, Uppot RN. CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept. Cardiovasc Intervent Radiol. 2019 Dec;42(12):1771-1776. doi: 10.1007/s00270-019-02336-0. Epub 2019 Sep 5. PMID: 31489473.
3. Daye D, Tabari A, Kim H, Chang K, Kamran SC, Hong TS, Kalpathy-Cramer J, Gee MS. Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer. Eur Radiol. 2021 Aug;31(8):5759-5767. doi: 10.1007/s00330-020-07673-0. Epub 2021 Jan 16. PMID: 33454799.
AUR Memorial Award, Association of University Radiologists
Stanley J. Korsmeyer Young Investigator Award, Association of American Physicians
Junior Fellow, International Society of Magnetic Resonance in Medicine
40 under 40 MedTech Boston Healthcare Innovator Award
RSNA Roentgen Research Award, Radiological Society of Northern America