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A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer

Objective: To evaluate the clinicopathologic characteristics of the long-time survivals and construct a clinical nomogram using the Surveilance, Epidemiology, and End Results (SEER) database. Materials and Methods: Information of patients diagnosed with M1 stage esophageal cancer from 2010-2014 was...

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Autores principales: Liu, Mina, Wang, Changlu, Gao, Lanting, Lv, Changxing, Cai, Xuwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218766/
https://www.ncbi.nlm.nih.gov/pubmed/30410603
http://dx.doi.org/10.7150/jca.27579
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author Liu, Mina
Wang, Changlu
Gao, Lanting
Lv, Changxing
Cai, Xuwei
author_facet Liu, Mina
Wang, Changlu
Gao, Lanting
Lv, Changxing
Cai, Xuwei
author_sort Liu, Mina
collection PubMed
description Objective: To evaluate the clinicopathologic characteristics of the long-time survivals and construct a clinical nomogram using the Surveilance, Epidemiology, and End Results (SEER) database. Materials and Methods: Information of patients diagnosed with M1 stage esophageal cancer from 2010-2014 was retrieved from SEER database. Patients with unknown information of AJCC TNM stage or metastatic sites or marital status or surgery or survival were excluded. Demographic and clinicopathologic characteristics were compared between LTS (long time survivals: patients who have survived for no less than 2 years) and STS (shorter time survivals: patients who have survived for less than 2 years). Cox regression analysis was performed to evaluate prognostic factors. A nomogram comprising demographic and clinicopathologic factors was established to predict 1-year survival and 2-year survival for patients with M1 diseases. Results: A total of 2981 patients from the SEER database were included for analysis. Compared with the STS, married people and patients with well differentiated tumors or oligometastatic site were more likely to be LTS. Also, LTS were associated with significantly less bone metastasis and more surgery. The OS nomogram, which had a c-index of 0.633, was based on the eleven variables: gender, age, marital status, T stage, N stage, histology, grade, number of important metastatic organs and primary surgery. Conclusions: Married patients, patients with well differentiated tumors, patients with oligometastatic site, patients without bone metastasis or liver metastasis and those who underwent surgery are associated with long time survivals. We developed a nomogram predicting 1- and 2-year OS and CSS for M1 stage esophageal cancer. The prognostic model may improve clinicians' abilities to predict individualized survival and to make treatment recommendations.
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spelling pubmed-62187662018-11-08 A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer Liu, Mina Wang, Changlu Gao, Lanting Lv, Changxing Cai, Xuwei J Cancer Research Paper Objective: To evaluate the clinicopathologic characteristics of the long-time survivals and construct a clinical nomogram using the Surveilance, Epidemiology, and End Results (SEER) database. Materials and Methods: Information of patients diagnosed with M1 stage esophageal cancer from 2010-2014 was retrieved from SEER database. Patients with unknown information of AJCC TNM stage or metastatic sites or marital status or surgery or survival were excluded. Demographic and clinicopathologic characteristics were compared between LTS (long time survivals: patients who have survived for no less than 2 years) and STS (shorter time survivals: patients who have survived for less than 2 years). Cox regression analysis was performed to evaluate prognostic factors. A nomogram comprising demographic and clinicopathologic factors was established to predict 1-year survival and 2-year survival for patients with M1 diseases. Results: A total of 2981 patients from the SEER database were included for analysis. Compared with the STS, married people and patients with well differentiated tumors or oligometastatic site were more likely to be LTS. Also, LTS were associated with significantly less bone metastasis and more surgery. The OS nomogram, which had a c-index of 0.633, was based on the eleven variables: gender, age, marital status, T stage, N stage, histology, grade, number of important metastatic organs and primary surgery. Conclusions: Married patients, patients with well differentiated tumors, patients with oligometastatic site, patients without bone metastasis or liver metastasis and those who underwent surgery are associated with long time survivals. We developed a nomogram predicting 1- and 2-year OS and CSS for M1 stage esophageal cancer. The prognostic model may improve clinicians' abilities to predict individualized survival and to make treatment recommendations. Ivyspring International Publisher 2018-10-11 /pmc/articles/PMC6218766/ /pubmed/30410603 http://dx.doi.org/10.7150/jca.27579 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Liu, Mina
Wang, Changlu
Gao, Lanting
Lv, Changxing
Cai, Xuwei
A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer
title A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer
title_full A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer
title_fullStr A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer
title_full_unstemmed A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer
title_short A nomogram to predict long-time survival for patients with M1 diseases of esophageal cancer
title_sort nomogram to predict long-time survival for patients with m1 diseases of esophageal cancer
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218766/
https://www.ncbi.nlm.nih.gov/pubmed/30410603
http://dx.doi.org/10.7150/jca.27579
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