LI-RADS TRA v2024 succeeds where RECIST and mRECIST fail: viability-based survival prediction in TACE-treated hepatocellular carcinoma
摘要
Despite robust response assessment validation of Liver Imaging Reporting and Data System Treatment Response Algorithm (LI-RADS TRA) version 2024, a critical evidence gap exists for its prognostic value in non-radiation hepatocellular carcinoma (HCC) therapies. Conventional response assessment criteria (RECIST, mRECIST) focus on morphologic changes, not tumor viability.
PurposeTo evaluate the prognostic implications of LI-RADS TRA v2024 versus RECIST and mRECIST for predicting overall survival and time to progression in HCC patients treated with trans-arterial chemoembolization (TACE), and to assess whether quantitative enhancement reduction augments prognostic stratification.
MethodsA total of 105 HCC patients undergoing TACE with multi-phasic contrast-enhanced CT were included. Four board-certified radiologists independently assessed tumor response using LI-RADS TRA v2024, RECIST, and mRECIST criteria.
ResultsLI-RADS TRA demonstrated convergent prognostic discrimination for overall survival, with non-responders achieving mean survival of 719.8 days versus responders 1,002.1 days (p = 0.014; hazard ratio 1.63). In contrast, RECIST (p = 0.670, hazard ratio 0.72) and mRECIST (p = 0.457, hazard ratio 1.16) showed negligible stratification. Importantly, LI-RADS TRA exhibited weak discrimination for time to progression (p = 0.095). Incorporating quantitative enhancement-size change from pre-TACE to post-TACE (%) substantially enhanced LI-RADS TRA prognostic performance for survival (HR 1.91, p = 0.026), but not progression prediction.
ConclusionLI-RADS TRA v2024 outperforms conventional size-based and enhancement-duration criteria for mortality risk stratification in TACE-treated HCC, reflecting mechanistic potential of viability-based assessment. However, imaging response predicts cumulative mortality, not progression timing. Integration of quantitative enhancement metrics refines risk stratification for surveillance planning.