Keywords: Liver, Data Analysis
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.
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