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Last Updated: 11/21/19
Quantitative Imaging Network (QIN)

Anant Madabhushi. Case Western Reserve University.

Anant Madabhushi is currently the F. Alex Nason Professor II of Biomedical Engineering at Case Western Reserve University, as well as the Director of the Center of Computational Imaging and Personalized Diagnostics (CCIPD). He is also a research scientist at the Louis Stokes Cleveland Veterans Administration Medical Center and have secondary appointments in 6 different departments including radiology, pathology, urology, radiation oncology, electrical engineering and computer science and general medical sciences. Dr. Madabhushi also has a secondary affiliation with the Cleveland Clinic.

CCIPD currently includes two tenure-track faculty members, three research faculty, 15 post-doctoral researchers, 20 graduate students, two scientific developers, four full time staff and several undergraduate researchers. For the last fifteen years he has been working in the area of computerized image analysis and detection. The goal is to enable computer image analysis systems to automatically detect and identify different types of disease — including prostate, breast, lung, and oropharyngeal cancer — based on an analysis of images produced by a variety of modalities. Over this period, his lab has received over $35 million in federal, state, foundation, and corporate funding. His group is recognized around the world as a leader in computerized imaging research and have demonstrated and built several new technologies for cancer detection and diagnosis. Dr. Madabhushi was called out by Nature Magazine as one of 5 scientists developing “Offbeat approaches for cancer research” in March 2019.

His research in the domains of computerized diagnosis, medical image analysis and computer vision has been motivated by the need for a systems based approach to disease understanding which aims to move away from a reductionist focus on a limited number of molecular components as has been the modern trend in studying diseases such as cancer, to a comprehensive understanding of how large numbers of interrelated health variables (proteomics, metabolites, genomics) result in emergence of definable phenotypes.

A major thrust of the group has been in developing computational image analysis and machine learning tools and applying them to high dimensional imaging and pathology data to uncover “sub-visual” image attributes that can inform on disease presence, disease risk, disease outcome and the likelihood of disease progression. The discovery of these computationally derived image biomarkers is already paving the way for disease prognostics, allowing physicians to predict as to which patient may be susceptible to a particular disease and also predicting disease outcome and survival. This work on discovery of image biomarkers is also being applied by the group to the field of “radiogenomics” or predicting the mutational status or molecular subtype of a tumor based solely off computational features derived from a routine imaging scan. Additionally, they have pioneered the Ibris (image based risk score (Ibris)) which allows for computing a probability of disease aggressiveness based off features mined from medical images for a variety of cancers.

Dr. Madabhushi has a long and productive history working in translational research and also in commercialization of biomedical imaging technology. His group has been averaging 1 patent issuance a month over the last 16 months. He was the co-founder of Ibris Inc., a digital pathology based company in 2010. The company licensed technologies relating to digital pathology and image based risk prediction (USSN: 9,002,092; 9,235,891; 9,286,672) from routine H&E tissue slides of breast cancer. His group is also developing similar technologies in the context of risk stratification of prostate, oral and gynecological cancers and hopes to disseminate these technologies and get them into clinical practice within the next 3-5 year time frame. Apart from cancer, the group has also recently begun to develop computational imaging and AI technologies for risk stratification and therapy response assessment for kidney, cardiovascular and eye disease.