ArticleThoracic cancer2022
The risk and prognostic factors for liver metastases in esophageal cancer patients: A large-cohort based study.
Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Anlotinib combined with benmelstobart as a chemo-free first-line treatment in advanced esophageal squamous cell carcinoma: an exploratory multicenter, single-arm phase II clinical trial.Molecular cancer · 2025Trial
- Machine learning to predict distant metastasis risk and prognostic analysis in T1-2 esophageal cancer: a study based on the SEER database.Journal of thoracic disease · 2026Article
- Prediction of Early Mortality in Esophageal Cancer Patients with Liver Metastasis Using Machine Learning Approaches.Life (Basel, Switzerland) · 2024Article
- Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.Journal of cancer research and clinical oncology · 2024Article
- Comparing the Efficacy of Carboplatin plus 5-Fluorouracil, Cisplatin plus 5-Fluorouracil, and Best Supportive Care for Advanced Esophageal Squamous Cell Carcinoma: A Propensity Score Analysis from a Tertiary Hospital in Southern Thailand.Journal of clinical medicine · 2024Article
- The risk and prognostic factors for liver metastases in esophageal cancer patients: A large-cohort based study.Thoracic cancer · 2022Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis retrospective study aimed to explore risk factors for liver metastases (LiM) in patients with esophageal cancer (EC) and to identify prognostic factors in patients initially diagnosed with LiM.
methodsA total of 28 654 EC patients were retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2018. A multivariate logistic regression model was utilized to identify risk factors for LiM. A Cox regression model was used to identify prognostic factors for patients with LiM.
resultsOf 28 654 EC patients, 4062 (14.2%) had LiM at diagnosis. The median overall survival (OS) for patients with and without LiM was 6.00 (95% CI: 5.70-6.30) months and 15.00 (95% CI: 14.64-15.36) months, respectively. Variables significantly associated with LiM included gender, age, tumor site, histology, tumor grade, tumor size, clinical T stage, clinical N stage, bone metastases (BoM), brain metastases (BrM) and lung metastases (LuM). Variables independently predicting survival for EC patients with LiM were age, histology, tumor grade, BoM, BrM, LuM, and chemotherapy. A risk prediction model and two survival prediction models were then constructed revealing satisfactory predictive accuracy.
conclusionsBased on the largest known cohort of EC, independent predictors of LiM and prognostic indicators of survival for patients with LiM were identified. Two models for predicting survival as well as a risk prediction model were developed with robust predictive accuracy.
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