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Images of Polyps Plus Tissue Data May Help Predict Cancer

Liangjerome2 1

By using computed tomography colonography (CTC), also known as virtual colonoscopy, to image many types of polyps, and matching those images to detailed gene expression data of those polyps, researchers may be able to determine what types of polyps will turn into cancer and which may not.

Jerome Liang, PhD
Jerome Liang, PhD

The work is a research collaboration between Stony Brook University, led by Jerome Liang, PhD, and the University of Wisconsin School of Medicine and Public Health, led by Perry J. Pickhardt, MD, and  is supported by a new $3 million grant from the National Institutes of Health (NIH). The project, titled “Radiogenomics of Colorectal Polyps to Assess Benign Proliferative vs. Premalignant States,” will involve taking hundreds of detailed high-tech images of polyps and also defining the many gene mutations that occur in polyp formations.

The ultimate goal is to predict by CTC imaging of polyps alone which types of polyps are ‘born to be bad,’ and therefore advance early detection and help clinicians determine their course of action to prevent cancer. Part of this research builds on the team’s ongoing research supported by a previous $2 million NIH grant using CTC to differentiate between polyp types. The new project focuses more on polyp biology. In combination, their research is expected to generate increased knowledge to help clinicians predict how polyps originate and in what way they are likely to develop.

Dr. Liang is Professor of Radiology, Computer Science and Biomedical Engineering, and Director of the Laboratory for Imaging Research and Informatics.

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