Combining serum biomarkers and MRI radiomics to predict treatment outcome after thermal ablation in hepatocellular carcinoma.

Abstract

Objective: To investigate the predictive value of serum alpha - fetoprotein (AFP), lectin-reactive alpha-fetoprotein (AFP-L3), and multimodal magnetic resonance imaging (MRI) radiomics in forecasting therapeutic efficacy and prognosis following radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC).

Methods: A retrospective analysis was conducted on HCC patients who underwent RFA between January 2019 and December 2023. Clinical and radiologic features of HCC were analyzed. A predictive model was developed using clinical data and radiomic features collected before surgery, with the goal of predicting prognosis after RFA. The predictive performance of the model was evaluated using AUC values in both training and validation cohorts.

Results: A total of 298 HCC patients were included in the study, divided into a good prognosis group (n=145) and a poor prognosis group (n=153). Serum AFP and AFP-L3 levels were significantly higher in the poor prognosis group (P=0.007 and P=0.02, respectively). Independent predictive factors included: AFP-L3 (95% CI -1.228, -1.1.61; P<0.001), AFP (95% CI 0.017, 0.036; P<0.001), intratumoral hemorrhage (95% CI 0.380, 0.581; P<0.001), peritumoral arterial tumor enhancement (95% CI 0.193, 0.534; P<0.001) and low signal intensity around liver and gallbladder tumors (95% CI 0.267, 0.489; P<0.001). The combined clinical-radiological-radiomics model demonstrated superior predictive performance, with AUC value of 0.897 in the training set and 0.841 in the validation set, outperforming individual models and sequences.

Conclusion: The integrated clinical-radiological-radiomics model showed excellent predictive performance for the prognosis of HCC patients undergoing RFA, surpassing individual models. Key predictors included serum AFP, AFP-L3 levels, intratumoral hemorrhage, and peritumoral low signal intensity. This multimodal approach offers a promising tool for individualized prognostic assessment and improved clinical decision-making.