LI-RADS® ancillary features on CT and MRI.

Victoria ChernyakAn TangMilana FlusbergDemetri PapadatosBijan BijanYuko KonoCynthia Santillan

Published in: Abdominal radiology (New York) (2019)

The Liver Imaging Reporting and Data System (LI-RADS) uses an algorithm to assign categories that reflect the probability of hepatocellular carcinoma (HCC), non-HCC malignancy, or benignity. Unlike other imaging algorithms, LI-RADS utilizes ancillary features (AFs) to refine the final category. AFs in LI-RADS v2017 are divided into those favoring malignancy in general, those favoring HCC specifically, and those favoring benignity. Additionally, LI-RADS v2017 provides new rules regarding application of AFs. The purpose of this review is to discuss ancillary features included in LI-RADS v2017, the rationale for their use, potential pitfalls encountered in their interpretation, and tips on their application.

Keyphrases

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machine learning
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magnetic resonance imaging
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deep learning
computed tomography
clinical trial
emergency department
electronic health record
big data
climate change
image quality
artificial intelligence
diffusion weighted imaging
dual energy
neural network