A New Conception About Individualized Treatment Allocation for HCC

A New Conception About Individualized Treatment Allocation for HCC-Using Machine Learning

The current guidelines on hepatocellular carcinoma (HCC) aimed to build effective prognostic stratification strategies to guide therapeutic allocation; however, the current guidelines did not consider the simultaneous comparison of distinct therapies in similar populations. Here, the investigators aimed to develop and validate a new, integrated prognostic scheme for HCC patients using artificial intelligence (AI) to simulate the survival outcomes of patients allocated to different treatments.

Given that liver resection (LR) and transarterial chemoembolization (TACE) are the mainstay curative and palliative therapies for HCC, respectively, patients who underwent LR or TACE were included in the study. Various prognostic AI algorithms were modeled using data from a large multi-institutional cohort, where LR and TACE were considered independent factors. The C-index, Brier score (BS), and area under the receiver operating characteristic curve (auROC) were calculated to estimate the AI models.

Inclusion Criteria

• Diagnosed with HCC
• Receiving LR or TACE therapy
• Complete clinical information
• Preserved liver function (Child-Pugh Score [CPS] ≤ 7)
• PS 0-1

Exclusion Criteria

• Vascular invasion or extrahepatic spread;
• Ecompensated liver cirrhosis ;
• Younger than 18 years;
• Duration of follow-up of fewer than 30 days.