Diagnostic Performance of LI-RADS v2024 Radiation Treatment Response Algorithm: A Multi-Reader Study with Pathologic Correlation
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.
Authors
- William Han
- Charles H. Adler (ORCID: https://orcid.org/0000-0002-1045-2743)
- C. Wang
- Alvin Silva
- Nan Zhang (ORCID: https://orcid.org/0000-0002-4996-9539)
- Maria Zulfiqar (ORCID: https://orcid.org/0000-0002-8662-3799)
- Kumar Sandrasegaran
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/1080
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00