Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation

Yun Zhang; Jinju Wang; Hui Li; Tianying Zheng; Hanyu Jiang; Mou Li; Bin Song

doi:10.21037/atm.2020.03.120

Performance of LI-RADS version 2018 CT treatment response algorithm in tumor response evaluation and survival prediction of patients with single hepatocellular carcinoma after radiofrequency ablation

Ann Transl Med. 2020 Mar;8(6):388. doi: 10.21037/atm.2020.03.120.

Authors

Yun Zhang¹, Jinju Wang², Hui Li², Tianying Zheng¹, Hanyu Jiang¹, Mou Li¹, Bin Song¹

Affiliations

¹ Department of Radiology, Sichuan University West China Hospital, Chengdu 610041, China.
² Department of Liver Surgery & Liver Transplantation Center, West China Hospital, Chengdu 610041, China.

Abstract

Background: The Liver Imaging Reporting and Data System treatment response algorithm (LI-RADS TRA) was developed to evaluate the tumor response of patients with hepatocellular carcinoma (HCC) after locoregional treatments. This study aimed to evaluate the performance of LI-RADS computed tomography (CT) TRA version 2018 in tumor response assessment and survival prediction of patients with single HCC after radiofrequency ablation (RFA).

Methods: Forty patients who underwent RFA for single HCC between 2010 and 2016 were included in this retrospective study. The overall survival (OS) data from all the patients after the first therapy was collected. Two readers independently assessed the pretreatment (within 7 d) and posttreatment (within 90 d after RFA) CT manifestations using the LI-RADS version 2018 CT TRA. Inter-reader agreement was assessed. Another radiologist re-evaluated any divergent results and came to the final conclusion. The performance of LI-RADS version 2018 CT TRA for tumor response assessment and predicting survival of patients with single HCC after RFA was evaluated.

Results: Interobserver agreement was moderate between the 2 readers [κ=0.602, 95% confidence interval (CI): 0.390-0.814] when using LI-RADS version 2018 TRA to evaluate tumor response for patients with single HCC after RFA. Patients classified as LR-TR viable had significantly lower OS than those classified as LR-TR nonviable (P=0.005) and LR-TR equivocal (P=0.036). However, the OS between LR-TR nonviable and LR-TR equivocal did not differ significantly (P=0.901).

Conclusions: LI-RADS version 2018 CT TRA can be applied to predict viable or nonviable HCC after RFA. Patients with LR-TR viable had significantly lower OS than those with LR-TR nonviable and LR-TR equivocal. More research is needed to validate the performance of LI-RADS version 2018 TRA in HCC tumor response evaluation, to better grasp the use of the tie-breaking rule, and to improve the accuracy of prediction for tumor viability.

Keywords: Hepatocellular carcinoma (HCC); Liver Imaging Reporting and Data System (LI-RADS); computed tomography (CT); radiofrequency ablation (RFA); treatment response algorithm (TRA).