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.

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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
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article

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

William Han, Charles H. Adler, C. Wang, Alvin Silva et al.
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
Radiomics and Machine Learning in Medical Imaging
article

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

William Han, Charles H. Adler, C. Wang, Alvin Silva, Nan Zhang, Maria Zulfiqar, Kumar Sandrasegaran
article en

Abstract

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.

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
Openalex Percentile: Top 64%
Radiomics and Machine Learning in Medical Imaging
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Diagnostic Performance of LI-RADS v2024 Radiation Treatment Response Algorithm: A Multi-Reader Study with Pathologic Correlation — William Han, Charles H. Adler, et al. · 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 (2025) | TGRS Research Map | TGRS