
David R. Entenberg
Associate Professor · Photonics-based assays
Albert Einstein College of MedicineAbout
David R. Entenberg is an Associate Professor in the Department of Pathology at Albert Einstein College of Medicine. He also serves as Co-Director of the Gruss-Lipper Biophotonics Center and holds leadership roles in the Integrated Imaging Program, including Director of Technology Development and Leader of the Computational Analysis of Biomarkers Program. His research focuses on developing photonics-based assays to study cancer metastasis, with specialties in intravital imaging, quantitative digital pathology, and biomarker validation. He has pioneered surgical engineering techniques for imaging metastasis in live models, including lung and pancreatic imaging windows. Dr. Entenberg's work bridges physics and biology, leveraging expertise in optical and mechanical engineering to advance cancer research tools.
His research interests include optimizing mouse models for real-time metastasis visualization, designing implantable microdevices to alter tumor microenvironments, and applying multiphoton microscopy to study tumor-cell interactions. He co-authored key primers on multiphoton imaging and teaches at Einstein's Quantitative Imaging of Cells course. His lab's innovations have led to breakthroughs in understanding metastatic pathways, macrophage roles in cancer progression, and racial disparities in breast cancer outcomes.
Dr. Entenberg's publications highlight advancements in tumor microenvironment analysis, biomarker validation (e.g., TMEM scoring), and preclinical therapy testing using Listeria-based delivery systems. His collaborative projects span breast, pancreatic, and thyroid cancer models, with a focus on translating imaging technologies into clinical applications.
His lab is actively developing novel imaging platforms and biomarker tools to improve metastasis prediction and therapeutic targeting. Current efforts include refining intravital imaging techniques for prolonged observation of metastatic processes and enhancing computational methods for analyzing tumor cell behavior in vivo.
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