ArticleFrontiers in medicine2023
Development and validation of a diagnostic and prognostic model for lung metastasis of hepatocellular carcinoma: a study based on the SEER database.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 7 citations in OpenAlex.
- Machine learning models for predicting survival in patients of hepatocellular carcinoma with second primary malignancy.Translational cancer research · 2025Article
- Article
- Development of a predictive model for lung metastasis in hepatocellular carcinoma patients: insights from SEER database analysis.Updates in surgery · 2025Article
- Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study.Discover oncology · 2025Article
- Analysis of Factors Predicting the Real-World Efficacy of Atezolizumab and Bevacizumab in Patients with Advanced Hepatocellular Carcinoma.Gut and liver · 2024Article
- Thyroid Gland as a Metastatic Site for Hepatocellular Carcinoma: A Rare Case Report.OncoTargets and therapy · 2024Article
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5 authors at 2 institutions in 1 country.
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Abstract
Background: Lung metastasis (LM) is a common occurrence in patients with hepatocellular carcinoma (HCC), and it is associated with a poorer prognosis compared to HCC patients without LM. This study aimed to identify predictors and prognostic factors for LM in HCC patients as well as develop diagnostic and prognostic nomograms specifically tailored for LM in HCC patients. Methods: A retrospective analysis was conducted on HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2015. The study employed multivariate logistic regression analysis to identify risk factors associated with LM in HCC patients. Additionally, multivariate Cox proportional hazards regression analysis was utilized to investigate prognostic factors for HCC patients with LM. Subsequently, two nomograms were developed to predict the risk and prognosis of LM in HCC patients. The performance of the nomograms was evaluated through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Result: This retrospective study included a total of 5,934 patients diagnosed with HCC, out of which 174 patients were diagnosed with LM. Through multivariate logistic regression analysis, several independent risk factors for LM in HCC patients were identified, including tumor grade, tumor size, American Joint Committee for Cancer (AJCC) T stage, and AJCC N stage. Furthermore, multivariate Cox analysis revealed that tumor grade, delayed treatment, surgery, and radiation were independent prognostic factors for HCC patients with LM. To assess the predictive power of the developed nomograms, calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were employed. The findings demonstrated that the nomograms exhibited satisfactory performance in both the training and validation sets. Additionally, the prognostic nomogram effectively stratified HCC patients with LM into low- and high-risk groups for mortality. Conclusion: These two nomograms optimally predicted the risk and prognosis of LM in HCC patients. Both nomograms have satisfactory performance. This would help clinicians to make accurate clinical decisions.
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