Open AccessThe risk and prognostic factors for liver metastases in esophageal cancer patients: A large‐cohort based study
Author(s) -
Luo Peng,
Wei Xiufeng,
Liu Chen,
Chen Xiankai,
Yang Yafan,
Zhang Ruixiang,
Kang Xiaozheng,
Qin Jianjun,
Qi Xiuzhu,
Li Yin
Publication year - 2022
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.14642
Subject(s) - medicine , logistic regression , oncology , proportional hazards model , stage (stratigraphy) , retrospective cohort study , epidemiology , multivariate analysis , cohort , cancer , esophageal cancer , t stage , paleontology , biology
Background This 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. Methods A 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. Results Of 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. Conclusions Based 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.