AUTHOR=Wang Ruizhi , Xu Hengtian , Chen Wufei , Jin Liang , Ma Zhuangxuan , Wen Lei , Wang Hongwei , Cao Kun , Du Xia , Li Ming 

TITLE=Gadoxetic acid–enhanced MRI with a focus on LI-RADS v2018 imaging features predicts the prognosis after radiofrequency ablation in small hepatocellular carcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.975216

DOI=10.3389/fonc.2023.975216

ISSN=2234-943X

ABSTRACT=Gadoxetic acid-enhanced MRI are contributed to evaluating the prognosis of small hepatocellular carcinoma (sHCC) following treatment. We have investigated the potential role of gadoxetic acid-enhanced MRI based on LI-RADS v2018 imaging features in the prognosis prediction of sHCC patients received with radiofrequency ablation (RFA) as the first-line treatment, and formulated a predictive nomogram. A total of 204 patients with sHCC patients who all received RFA as the first-line therapy were enrolled. All patients had undergone gadoxetic acid-enhanced MRI examinations before RFA. Uni- and multivariable analyses for RFS were assessing using a Cox proportional hazards model. A novel nomogram was further constructed for predicting RFS. The clinical capacity of the model was validated according to calibration curves, the C-index and decision curve analyses. AFP level (HR 2.006, 95%CI 1.111-3.621, P = 0.021), rim APHE (HR 2.751, 95%CI 1.511-5.011, P = 0.001), targetoid restriction on DWI (HR 3.289, 95%CI 1.832-5.906, P < 0.001) were considered as independent risk features for recurrence in sHCC patients treated with RFA. The calibration curves and C-indexes (C-index values of 0.758 and 0.807) showed the superior predictive performance of the integrated nomogram in both training and validation groups. Gadoxetic acid-enhanced MRI feature based on LI-RADS v2018, including rim APHE and targetoid restriction on DWI, and AFP level are the independent risk factors of recurrence in sHCC received RFA as the first-line therapy. The predictive clinical-radiological nomogram model was constructed for clinicians to develop individualized treatment and surveillance strategies.