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Reuters Health Information: Updated algorithm for managing incidental liver lesions on CT

Updated algorithm for managing incidental liver lesions on CT

Last Updated: 2017-10-06

By Reuters Staff

NEW YORK (Reuters Health) - Size and radiological features should determine how to manage liver lesions detected incidentally on CT scan, says the American College of Radiology (ACR) Incidental Findings Committee in an updated algorithm.

Incidental liver lesions will be detected on CT scan in up to 30% of people older than 40. Most lesions are benign, but further workup can be difficult to avoid, according to the September 16 Journal of the ACR online report.

Dr. Richard M. Gore from NorthShore University HealthSystem, Evanston, Illinois, and colleagues present recommendations for managing these lesions, updated from 2010. The recommendations are based on published evidence, expert opinion, and informal iterative consensus.

The updated algorithm categorizes liver lesions according to size, patient characteristics, and imaging features. Each branch leads to an assessment of benignity or a specific follow-up recommendation.

Four recommendations define the updated algorithm for incidental hepatic lesions: - forgo workup of lesions <1 cm in low-risk patients - forgo workup of lesions with distinctly benign features, regardless of patient risk level - pursue workup of lesions that are at least 1 cm and do not have distinctly benign features in high-risk patients - use MRI for further workup.

The committee notes that the algorithm aims to distinguish benign from potentially malignant incidental findings - and not hepatic infections or abscesses, which are likely to be associated with clinical signs or symptoms.

"The algorithm addresses most, but not all, pathologies and clinical scenarios," the authors note. "The goal is to improve the quality of care by providing guidance on how to manage incidentally detected liver lesions."

Dr. Gore did not respond to a request for comment.

SOURCE: http://bit.ly/2yusJGZ

J Am Coll Radiol 2017.

 
 
 
 
                         
 
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