Adjuvant transarterial chemoembolization (TACE) is widely adopted in China for resectable hepatocellular carcinoma (HCC), yet its efficacy remains inconsistent. We aimed to identify factors influencing individual patient benefit using causal machine learning. To this end, we retrospectively collected HCC patients with high risk factors for tumor recurrence from four centers of China, divided into the discovery cohort and the validation cohort . The primary endpoint was disease-free survival (DFS). The primary endpoint was overall survival (OS).Individual treatment effects (ITEs) were estimated within a causal machine learning framework. An ITE \< 0 was considered recommendation for adjuvant TACE , while ITE ≥ 0 indicated active surveillance. The model would be validated in the validation cohort. The contribution of each variable to ITE was assessed using the Shapley Additive Explanations (SHAP). An online calculator would be developed for future use by public.
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Disease free survival
Timeframe: From January 2018 to December 2023