Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review.

backgroundRecurrence prediction of hepatocellular carcinoma (HCC) after thermal ablation represents a challenge that can impact patients' quality of life. Artificial intelligence (AI)-based radiomics models applied to various imaging modalities can improve recurrence prediction, therefore guiding therapeutic decisions.

aimTo evaluate the effectiveness of AI-driven predictive models in predicting HCC recurrence.

methodsA systematic literature search in PubMed and Scopus was performed, and a total of ten studies were included in this systematic review. All studies included response prediction evaluation with AI models for patients who underwent thermal ablation for HCC. Deep learning and machine learning algorithms were utilized to evaluate the predictive performance and accuracy through metrics such as the area under the curve and concordance index.

resultsThe developed models demonstrated high accuracy in predicting local progression and recurrence, allowing a solid risk stratification. In particular, the integration of imaging data and clinical-laboratory variables optimized treatment selection, highlighting the superior ability of imaging models to predict therapeutic outcomes compared to clinical parameters alone. Furthermore, radiomic analysis of follow-up imaging enabled highly accurate detection of ablation site recurrence.

conclusionAI-driven predictive models based on multimodal radiomic analyses integrated with clinical data represent promising tools for predicting tumor recurrence after thermal ablation in HCC patients.