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Sanjay Aneja, MD

Assistant Professor of Therapeutic Radiology; Director of Clinical Informatics, Therapeutic Radiology; Director Medical School Clerkship, Therapeutic Radiology; Medical School Thesis Oversight, Therapeutic Radiology; Radiation Safety, Therapeutic Radiology; Assistant Cancer Center Director, Bioinformatics

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Sanjay Aneja, MD

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Sanjay Aneja, MD is an Assistant Professor within the Department of Therapeutic Radiology at Yale School of Medicine. Dr. Aneja is a physician scientist whose research group is focused on the application of machine learning techniques on clinical oncology. He received his medical degree from Yale School of Medicine and served as class president. During medical school he completed a research fellowship at the Department of Health and Human Services in large scale data analysis. He later completed his medicine internship at Memorial Sloan Kettering Cancer Center followed by his residency in radiation oncology at Yale-New Haven Hospital. During his residency he completed his post-doc in machine learning at the Center for Outcomes Research and Evaluation (CORE) receiving research grant from IBM Computing. He is currently a recipient of an NIH Career Development award, an NSF research grant, and an American Cancer Society research award.

The Aneja Labs on-going efforts include:

1) Deep Learning to Derive Imaging Based Biomarkers of Cancer Outcomes: We have previously shown the ability for deep learning to derive imaging-based biomarkers for lung cancer and are currently applying our deep learning platform to brain metastases. We have developed a national consortium of 7 institutions whom have contributed data to our effort. This project is funded by the NIH, AHRQ, Radiation Society of North America (RSNA), and the American Cancer Society.

2) AI-Driven Collection of Patient Reported Outcomes: Our group is developing deep learning algorithms which use patient audio diaries to predict validated patient reported outcome metrics. Through a collaboration with Amazon, we hope to integrate our algorithm into virtual assistants and pilot them in a clinical setting.

3) Machine Learning Methods for Clinical Trial Classification: Our group, through a collaboration with SWOG and an industry partner, is studying the ability of machine learning to classify cancer clinical trials and match clinicians to relevant randomized clinical trials. This project is currently funded by the NSF and SWOG Hope Grant.

Education & Training

  • Radiation Oncology Resident
    Yale University School of Medicine (2018)
  • Postdoctoral Research Fellow
    Center for Outcomes Research (CORE) (2017)
  • Transitional Year Resident
    Memorial-Sloan Kettering Cancer Center (2014)
  • Research Fellow
    Center for Medicare Medicaid Innovation (CMMI) (2013)
  • MD
    Yale School of Medicine (2013)
  • BA
    Columbia University, Applied Mathematics (2009)

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