Computer Aided Detection for CT Colonography

Clinical Center

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Abstract

We are improving CT colonography (virtual colonoscopy) by developing computer-assisted diagnosis methods. These methods attempt to identify and characterize colonic polyps automatically, thereby increasing physician accuracy and efficiency and helping patients by finding their polyps. We are developing methods to detect extracolonic findings on CT colonography using fully-automated software. Examples include automated body composition analysis and bone mineral densitometry. We have improved the accuracy of such methods compared to earlier versions. In FY 2022, we made advances in several areas. In patients undergoing colorectal cancer (CRC) screening, we were able to (1) identify patients who might develop diabetes years before being diagnosed with diabetes, (2) automatically detect kidney stones and determine the volume and location of the stones and (3) show that the abdominal atherosclerotic plaque of prostate cancer patients did not differ from that of the CRC screening control group and was not correlated with any of the prostate cancer-related biomarkers.