Dr. Sanjeev Chawla is a research assistant professor in the Department of Radiology, Perelman School of Medicine, University of Pennsylvania. He is also a medical physicist certified by the American Board of Medical Physics. The focus of Dr. Chawla’s research has been directed toward the development of metabolic and physiological MR imaging-derived biomarkers in making correct diagnosis and assessing treatment responses to established, novel, and emerging therapies in patients with brain tumor, head and neck cancer, and neurodegenerative diseases.
He has a master’s degree in chemistry from the Indian Institute of Technology Delhi and a PhD in radiology from Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow. He has authored 103 peer-reviewed original research/review articles and eight book chapters. He has been awarded research grants by agencies like the National Institute of Health/National Cancer Institute, the International Society for Magnetic Resonance in Medicine, and the Penn Center for Precision Medicine. Currently, he is leading two clinical trials related to electric field therapy in glioblastomas (NCT05086497) and evaluation of treatment response in the case of salivary gland tumors (NCT04452162).
Dr. Chawla is also an associate editor with the Journal of Translational Medicine and a reviewer for several leading scientific journals. Earlier, he was a guest editor with Frontiers in Neurology. He has also won the Outstanding Researcher Award in Neuroradiology from the Venus International Foundation and the Leadership and Mentorship Scholarship Award from the National Cancer Institute Awardee Skill Development Consortia.
Dr. Chawla’s Fulbright-Nehru project is building a robust, reproducible, and objective clinical decision support (CDS) tool by incorporating physiologic and metabolic MR imaging-derived parameters and molecular signatures combined with machine learning algorithms for assessing treatment response in glioblastoma patients receiving standard treatment as well as novel therapies. This tool will not only facilitate accurate and timely differentiation of true progression and pseudo progression in glioblastomas (precision diagnostics) but also allow clinicians to make “go/stop” decisions on therapeutic interventions (precision therapeutics). Additionally, it will help to relieve “scanxiety” among patients and their loved ones.