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Reanalysis of Lymph node metastasis as predictor of the survival of patients with follicular variant papillary thyroid carcinoma: From SEER database 2004-2014

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摘要 Background: Follicular variant papillary thyroid carcinoma (FVPTC) is the most common subset of papillary carcinoma. Lymph-node metastasis (LNM) is one of the main predictors of survival in PTC patients, but we found few data related to the correlation between LNM and FVPTC. We therefore aimed to elucidate the relationship between LNM and survival in patients with FVPTC. Methods: Logistic regression models were used to determine the factors associated with LNM. The Cox proportional-hazards model was applied to assess the association between LNM and disease-specific mortality. Kaplan-Meier survival analysis and the log-rank test were used to estimate the disease-specific survival rates in patients with and without LNM. Results: In total, 1418 FVPTC patients were identified. The multivariate logistic regression model showed that age, female sex, being married, tumor size > 4cm and extrathyroidal extension and radiation were predictors of LNM. The multivariate Cox regression model showed that age, multifocality and radiation were risk factors of diseasespecific mortality. Especially, LNM (p=0.271) did not independently predict disease-specific mortality. Kaplan-Meier survival analysis and the log-rank method also showed that LNM did not impact survival in FVPTC (p=0.235). Conclusion: Using data in the SEER database from 2004 to 2014, and after multivariate model correction, we found that age, multifocality and radiation were strongly related to disease-specific mortality. It is worth noting that LNM does not impact the survival of FVPTC patients.
出处 《Medical Data Mining》 2019年第1期1-9,共9页 TMR医学数据挖掘
基金 National Social Science Foundation of China (No.16BGL183) Natural Science Foundation of Shaanxi Province (No.2015JM8415) Fundamental Research Funds for the Central Universities of China (No.2011jdhz55) Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University of China (No.XJTU1AF-CRF-2016-021) Research Fund of Health Bureau of Xi’an (No. QFO1330).
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