Computer Aided Detection for CT Colonography

$0ZIAFY2025CLNIH

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Abstract

We are developing methods to detect extracolonic findings on CT colonography using fully-automated software. Examples include automated body composition analysis, liver surface nodularity estimation, and liver fibrosis staging. We have improved the accuracy of such methods compared to earlier versions. In FY 2025, we made advances in several areas. In patients undergoing colorectal cancer (CRC) screening with CT, we were able to (1) assess changes in the pancreas associated with diabetes, (2) detect incidental liver fibrosis by assessing inflammatory changes in the fat surrounding the blood vessels feeding the liver and (3) increase the accuracy of body composition analysis.

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