Michael Feldman, MD, PhD

My professional interests revolve around the development of institutional biobanking as well as the integration and adoption of information technologies in the discipline of healthcare, and particularly pathology.

My work in the field of tissue banking included being an early funded adopter and tester in the NCI caBIG project, where we partnered on the testing of caTissue with the University of Pittsburgh and were the first cancer center using both caTissue and caTIES. caTIES has now morphed into a multicenter Tissue Collaborative Research Network (TCRN) and is NIH funded.

In the field of digital imaging, I have been developing computer-assisted diagnostic algorithms for machine vision in breast cancer and quantitative scoring of immunohistochemical and immunofluorescent stains of FFPE tissue. Our newest area of investigation is the use of Imaging mass cytometry of pancreas tissues and multispectral imaging for the analysis of multicolor immunohistochemistry and immunofluorescence and the development of a quantitative system for scoring and analyzing at a cytometric level, multicolor immunostaining on surgical pathology slides which was recently used in multiple of breast cancer publications listed below. The efforts have been recognized by the national funding agencies from the NIH, Synergy award from DOD, as well as an industry-sponsored projects.

1. Crowley RS, Castine M, Mitchell K, Chavan G, McSherry T, Feldman M. (2010). caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. J Am Med Informatics 17(3): 253-64. PMCID: PMC2995710
2. Jacobson RS, Becich MJ, Bollag RJ, Chavan G, Corrigan J, Dhir R, Feldman MD, Gaudioso C, Legowski E, Maihle NJ, Mitchell K, Murphy M, Sakthivel M, Tseytlin E, Weaver J: A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens. Cancer Res 75(24): 5194-5201, December 2015. PMCID: PMC4683415
3. Lee, G, Singanamalli, A, Wang, H, Feldman, M, Master, SR, Shih, N, Spangle, E, Rebbeck, T, Tomaszewski, J, & Madabhushi, A. (2015). Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer. IEEE Transactions on Medical Imaging, 34(1), 284-297. PMID: 25203987.
4. Nirschl JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, Madabhushi A: A deep- learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS One 3(13), April 2018. PMCID: PMC5882098