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Abstract #1080

Diagnostic Performance of LI-RADS v2024 Radiation Treatment Response Algorithm: A Multi-Reader Study with Pathologic Correlation

William Han1, Charis Wang1, Nan Zhang2, Cameron Adler3, Maria Zulfiqar3, Kumar Sandrasegaran3, and Alvin Silva3
1Mayo Clinic Alix School of Medicine, Mayo Clinic, Scottsdale, AZ, United States, 2Department of Quantitative Health Science Research, Mayo Clinic Arizona, Phoenix, AZ, United States, 3Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, United States

Synopsis

Keywords: Liver, Liver, Treatment Response, Tumor

Motivation: LI-RADS Treatment Response Algorithm (TRA) v2018 had limitations in assessing radiation-based therapies for hepatocellular carcinoma (HCC). The newly released v2024 includes specific criteria for radiation therapy response assessment.

Goal(s): To evaluate the diagnostic performance and inter-reader agreement of LI-RADS v2024 radiation TRA using pathologic correlation as the reference standard.

Approach: Multi-reader study with 4 fellowship-trained radiologists interpreting post-treatment imaging in 25 patients who underwent liver transplantation after radiation therapy.

Results: The radiation TRA demonstrated high specificity (91.7%) and moderate sensitivity (46.2-61.5%) across readers, with strong inter-reader agreement (Fleiss' kappa = 0.714).

Impact: This first validation study of LI-RADS v2024 radiation TRA shows excellent inter-reader agreement and diagnostic performance, supporting its clinical implementation for standardized post-radiation therapy response assessment in HCC patients.

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