This cohort study enrolled 243 patients with intermediate and advanced hepatocellular carcinoma to evaluate prognostic factors for tumor progression. The setting and specific publication type were not reported. The analysis utilized LASSO regression to screen for prognostic factors within this population.
The study identified total bilirubin, D-dimer, and portal vein tumor thrombosis as independent risk factors for tumor progression. Additionally, the LASSO regression screened 10 key prognostic factors: aspartate aminotransferase, total bilirubin, albumin, white blood cell count, D-dimer, alpha-fetoprotein, CD4+ T cell count, portal vein tumor thrombosis, tumor number (≥3), and lymph node invasion. The model demonstrated good discrimination ability, with a Log rank P value reported for the model's discrimination ability.
Safety data, including adverse events, serious adverse events, discontinuations, and tolerability, were not reported. The study did not provide absolute numbers or specific p-values for the risk factors beyond the Log rank P for the model. Limitations included the lack of reported adverse event data and the observational design, which precludes causal conclusions about the intervention of TACE combined with targeted immunotherapy.
The practice relevance of these findings is to guide individualized treatment decisions. However, because the study is a cohort study rather than a randomized trial, the results should be interpreted with caution regarding the specific intervention effects.
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BackgroundTranscatheter arterial chemoembolization (TACE) combined with targeted immunotherapy have become the standard first-line treatment strategy for intermediate and advanced hepatocellular carcinoma (HCC), but some patients still cannot benefit from this treatment. This study aimed to construct a prediction model based on a cohort of HCC patients to guide individualized treatment decisions.MethodsA total of 243 intermediate and advanced HCC patients who received TACE combined with targeted immunotherapy from January 1, 2019 to March 31, 2024 were retrospectively enrolled. The optimal prognostic factors were screened by Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression. A nomogram model for predicting the probability of radiologic progression-free survival at 6-, 12-, and 24-month was constructed based on the screened risk factors, and the model performance was evaluated by calibration curve, decision curve analysis, and restricted cubic spline analysis.ResultsMultivariate COX analysis showed that total bilirubin, D-dimer (DD) and portal vein tumor thrombosis (PVTT) were independent risk factors affecting the tumor progression of patients. LASSO regression screened out 10 key prognostic factors: aspartate aminotransferase, total bilirubin, albumin (ALB), white blood cell count, DD, alpha-fetoprotein, CD4+ T cell count, PVTT, tumor number (≥3) and lymph node invasion. Compared with the Cox regression model, significant advantages in screening prognostic factors were demonstrated by the LASSO regression model. Therefore, the risk factors identified by LASSO regression were chosen to construct a nomogram prediction model for subsequent analysis. The model showed good discrimination ability (Log rank P