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Front Oncol ; 14: 1405178, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38715786

RESUMO

The progression pattern of tumors has an impact on the survival of patients with advanced hepatocellular carcinoma (HCC) and has been applied in the design of clinical trials for multiple second-line drugs. Previous research results have been contradictory, and the clinical impact of different progression patterns and their role in survival are still in question. Purpose: The study aims to analyze the impact of different progression patterns and tumor burden size on survival of HCC patients, as well as their interactions, through a retrospective cohort study. Patients and methods: The study involved 538 patients who had undergone treatment with sorafenib and had shown radiographic progression. The progression pattern was analyzed using Cox regression by including an interaction term between progression pattern and tumor burden, which was then visualized through a graphical analysis. Tumor burden was categorized into low, medium, and high subgroups based on the six-and-twelve criteria, allowing for an exploration of the effect of progression pattern on survival in different tumor burden situations. Results: Compared to patients with only intrahepatic progression (NIH/IHG) with an overall survival (OS) of 14.1/19.9 months and post-progression survival (PPS) of 8.1/13.1 months respectively, patients with extrahepatic lesions (NEH/EHG) had worse overall and postprogressive survival (OS: 9.3/9.2 months, PPS: 4.9/5.1 months). The hazard ratio for extrahepatic progression (NEH/EHG) compared to intrahepatic progression (NIH/IHG) at low, medium, and high tumor burden were [HR 2.729, 95%CI 1.189-6.263], [HR 1.755, 95%CI 1.269-2.427], and [HR 1.117, 95%CI 0.832-1.499], respectively. Conclusion: The study concluded that the interaction between the tumor progression patterns and tumor burden significantly affects the prognosis of HCC patients. As the tumor burden increases, the sensitivity of the patient's risk of death to the progression pattern decreases. These findings are valuable in personalized treatment and trial design.

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