Predictive Model for Proliferative HCC Using LI-RADS v2018: Assessing Therapeutic Outcomes in Hepatectomy and Systemic Therapy

Motivation: Hepatocellular carcinoma (HCC) can be categorized into proliferative and non-proliferative classes, with proliferative HCC exhibiting aggressive characteristics and a poor prognosis. Goal(s): To develop a predictive model for proliferative HCC using Liver Imaging Reporting and Data System (LI-RADS) and to investigate its prognostic value for HCC. Approach: A logistic regression nomogram was constructed based on LI-RADS features to identify proliferative HCC. The implication of model-predicted proliferative HCC for different therapeutic outcomes in HCC was investigated. Results: The predictive model for proliferative HCC performed well and is a risk factor for postoperative recurrence in HCC, associated with favorable outcomes in systemic therapy. Impact: The MR-based model, utilizing LI-RADS v2018, could predict proliferative HCC before treatment. Patients with model-predicted proliferative HCC had more post-hepatectomy recurrences but better responses to systemic therapy, which may facilitate clinical decision-making for more precise and rational therapeutic strategies.