Advances in the LI-RADS Treatment Response Algorithm: Imaging Principles, Diagnostic Challenges, and Clinical Integration Following the LI-RADS 2024 Update.
Authors
Affiliations (6)
Affiliations (6)
- Programme of Radiology, Faculty of Allied and Healthcare Sciences, Assam Down Town University, Guwahati, Assam, India.
- Department of Urology and Renal Transplant, Gauhati Medical College and Hospital, Guwahati, Assam, India.
- Division of Pre-college and Undergraduate Studies, Brown University, Providence, RI, USA.
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300131, People's Republic of China.
- School of Engineering, Saveetha University, Chennai, India.
- Department of Pharmacy, The University of the Basque Country, Vitoria-Gasteiz, Spain.
Abstract
Hepatocellular carcinoma (HCC) management has evolved rapidly with the expanding use of locoregional therapies, systemic agents, and modern immunotherapy combinations. These advances have introduced complex and variable post-treatment imaging appearances, underscoring the need for refined, standardized criteria to accurately differentiate viable tumors from treatment-related changes. The updated LI-RADS Treatment Response Algorithm (v2024-2025) incorporates new guidance on arterial enhancement, washout, ancillary features, and modality-specific considerations to improve consistency and diagnostic clarity. This review synthesizes these updates and their implications for clinical practice. A comprehensive narrative review was conducted using updated LI-RADS guidelines and recent literature evaluating CT, MRI, CEUS, dual-energy CT, and photon-counting CT for post-treatment HCC assessment. Therapy-specific imaging appearances, algorithmic refinements, pitfalls, radiologic-pathologic correlations, and applications of artificial intelligence were analyzed. The updated LI-RADS TRA has shown potential lesion-level assessment by clarifying criteria for viable, non-viable, and equivocal responses. Multiparametric MRI and spectral CT improve the detection of subtle viable tissue, while advanced imaging helps address confounding factors such as granulation tissue, perfusional changes, and immune-related pseudo-progression. The algorithm demonstrates high accuracy after ablation and TACE, with ongoing refinements needed for radiotherapy and immunotherapy. Radiomics and machine learning models show strong potential for automated viability assessment and early prediction of recurrence. The LI-RADS TRA (v2024-2025) strengthens standardization of post-treatment HCC imaging and supports more accurate, reproducible interpretation across modalities. As treatment options expand, integrating quantitative imaging biomarkers and AI-driven tools will be essential for personalized treatment planning and improved clinical outcomes.