An intraprocedural CT–based AI pipeline can quantify ablative margins during radiofrequency ablation and predict local tumor progression after treatment for hepatocellular carcinoma. The retrospective case-control design describes an automated system that extracts and measures “ablative margins” on imaging acquired during the procedure, then links margin size to subsequent local outcomes. The practical implication is workflow support for real-time quality assessment rather than post-hoc review. For precision oncology stakeholders, the work reinforces growing adoption of computational decision support in ablation governance, where margin adequacy often determines re-treatment likelihood.