Objective To conduct a systematic review and Meta-analysis of predictive models for moderate-to-severe pain after interventional therapy for hepatocellular carcinoma (HCC) in China.

Methods CNKI, WangFang, VIP, SinoMed, PubMed, Embase, Web of Science, The Cochrane Library were electronically searched to collect literature relevant to the research objectives from inception to July 15, 2026. Two researchers extracted data, assessed the model and conducted the Meta-analysis by using the CHecklist for Critical Appraisal and Data Extration for Systematic Reviews of Prediction Modelling Studies (CHARMS), the Prediction Model Risk of Bias Assessment Tool-Artificial Intelligence (PROBAST+AI), and R software version 4.5.0.

Results A total of 16 studies involving 23 predictive models and 6,609 patients were included. The included studies showed good applicability but a high risk of bias. Meta-analysis results showed that the incidence of moderate-to-severe pain after liver cancer intervention was 34% [95%CI(26%, 43%)]. The overall model's predictive performance metric AUC was 0.80 [95%CI(0.77, 0.84)]. Gender was a factor influencing model performance. The primary predictors of moderate-to-severe pain after HCC interventional therapy were tumor number, tumor size, transarterial chemoembolization (TACE) procedure type, history of abdominal pain after TACE, vascular invasion, age, distance from the tumor to the liver capsule, use of ethanol embolization, iodized oil volume >10 mL, preoperative pain, and drug-eluting bead transarterial chemoembolization (DEB-TACE) (P< 0.05).

Conclusion Research on predictive models for moderate-to-severe pain after interventional therapy for HCC in China remains underdeveloped. Future studies should focus on patient gender distribution and integrate multiple machine learning modeling with multimodal data fusion pain assessment approaches to further optimize and externally validate existing models, thereby facilitating the translation of predictive tools into clinical precision pain management.

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