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Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer

Prognostic models are generally used to predict gastric cancer outcomes. However, no model combining patient-, tumor- and host-related factors has been established to predict outcomes after radical gastrectomy, especially outcomes of patients without nodal involvement. The aim of this study was to d...

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Autores principales: Qu, Jing-lei, Qu, Xiu-juan, Li, Zhi, Zhang, Jing-dong, Liu, Jing, Teng, Yue-e, Jin, Bo, Zhao, Ming-fang, Yu, Ping, Shi, Jing, Fu, Ling-yu, Wang, Zhen-ning, Liu, Yun-peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4468084/
https://www.ncbi.nlm.nih.gov/pubmed/26075713
http://dx.doi.org/10.1371/journal.pone.0128540
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author Qu, Jing-lei
Qu, Xiu-juan
Li, Zhi
Zhang, Jing-dong
Liu, Jing
Teng, Yue-e
Jin, Bo
Zhao, Ming-fang
Yu, Ping
Shi, Jing
Fu, Ling-yu
Wang, Zhen-ning
Liu, Yun-peng
author_facet Qu, Jing-lei
Qu, Xiu-juan
Li, Zhi
Zhang, Jing-dong
Liu, Jing
Teng, Yue-e
Jin, Bo
Zhao, Ming-fang
Yu, Ping
Shi, Jing
Fu, Ling-yu
Wang, Zhen-ning
Liu, Yun-peng
author_sort Qu, Jing-lei
collection PubMed
description Prognostic models are generally used to predict gastric cancer outcomes. However, no model combining patient-, tumor- and host-related factors has been established to predict outcomes after radical gastrectomy, especially outcomes of patients without nodal involvement. The aim of this study was to develop a prognostic model based on the systemic inflammatory response and clinicopathological factors of resectable gastric cancer and determine whether the model can improve prognostic accuracy in node-negative patients. We reviewed the clinical, laboratory, histopathological and survival data of 1397 patients who underwent radical gastrectomy between 2007 and 2013. Patients were split into development and validation sets of 1123 and 274 patients, respectively. Among all 1397 patients, 545 had node-negative gastric cancer; 440 were included in the development set, 105 were included in the validation set. A prognostic model was constructed from the development set. The scoring system was based on hazard ratios in a Cox proportional hazard model. In the multivariate analysis, age, tumor size, Lauren type, depth of invasion, lymph node metastasis, and the neutrophil—lymphocyte ratio were independent prognostic indicators of overall survival. A prognostic model was then established based on the significant factors. Patients were categorized into five groups according to their scores. The 3-year survival rates for the low- to high-risk groups were 98.9%, 92.8%, 82.4%, 58.4%, and 36.9%, respectively (P < 0.001). The prognostic model clearly discriminated patients with stage pT1-4N0M0 tumor into four risk groups with significant differences in the 3-year survival rates (P < 0.001). Compared with the pathological T stage, the model improved the predictive accuracy of the 3-year survival rate by 5% for node-negative patients. The prognostic scores also stratified the patients with stage pT4aN0M0 tumor into significantly different risk groups (P = 0.004). Furthermore, the predictive value of this model was validated in an independent set of 274 patients. This model, which included the systemic inflammatory markers and clinicopathological factors, is more effective in predicting the prognosis of node-negative gastric cancer than traditional staging systems. Patients in the high-risk group might be good candidates for adjuvant chemotherapy.
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spelling pubmed-44680842015-06-25 Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer Qu, Jing-lei Qu, Xiu-juan Li, Zhi Zhang, Jing-dong Liu, Jing Teng, Yue-e Jin, Bo Zhao, Ming-fang Yu, Ping Shi, Jing Fu, Ling-yu Wang, Zhen-ning Liu, Yun-peng PLoS One Research Article Prognostic models are generally used to predict gastric cancer outcomes. However, no model combining patient-, tumor- and host-related factors has been established to predict outcomes after radical gastrectomy, especially outcomes of patients without nodal involvement. The aim of this study was to develop a prognostic model based on the systemic inflammatory response and clinicopathological factors of resectable gastric cancer and determine whether the model can improve prognostic accuracy in node-negative patients. We reviewed the clinical, laboratory, histopathological and survival data of 1397 patients who underwent radical gastrectomy between 2007 and 2013. Patients were split into development and validation sets of 1123 and 274 patients, respectively. Among all 1397 patients, 545 had node-negative gastric cancer; 440 were included in the development set, 105 were included in the validation set. A prognostic model was constructed from the development set. The scoring system was based on hazard ratios in a Cox proportional hazard model. In the multivariate analysis, age, tumor size, Lauren type, depth of invasion, lymph node metastasis, and the neutrophil—lymphocyte ratio were independent prognostic indicators of overall survival. A prognostic model was then established based on the significant factors. Patients were categorized into five groups according to their scores. The 3-year survival rates for the low- to high-risk groups were 98.9%, 92.8%, 82.4%, 58.4%, and 36.9%, respectively (P < 0.001). The prognostic model clearly discriminated patients with stage pT1-4N0M0 tumor into four risk groups with significant differences in the 3-year survival rates (P < 0.001). Compared with the pathological T stage, the model improved the predictive accuracy of the 3-year survival rate by 5% for node-negative patients. The prognostic scores also stratified the patients with stage pT4aN0M0 tumor into significantly different risk groups (P = 0.004). Furthermore, the predictive value of this model was validated in an independent set of 274 patients. This model, which included the systemic inflammatory markers and clinicopathological factors, is more effective in predicting the prognosis of node-negative gastric cancer than traditional staging systems. Patients in the high-risk group might be good candidates for adjuvant chemotherapy. Public Library of Science 2015-06-15 /pmc/articles/PMC4468084/ /pubmed/26075713 http://dx.doi.org/10.1371/journal.pone.0128540 Text en © 2015 Qu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Qu, Jing-lei
Qu, Xiu-juan
Li, Zhi
Zhang, Jing-dong
Liu, Jing
Teng, Yue-e
Jin, Bo
Zhao, Ming-fang
Yu, Ping
Shi, Jing
Fu, Ling-yu
Wang, Zhen-ning
Liu, Yun-peng
Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer
title Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer
title_full Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer
title_fullStr Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer
title_full_unstemmed Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer
title_short Prognostic Model Based on Systemic Inflammatory Response and Clinicopathological Factors to Predict Outcome of Patients with Node-Negative Gastric Cancer
title_sort prognostic model based on systemic inflammatory response and clinicopathological factors to predict outcome of patients with node-negative gastric cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4468084/
https://www.ncbi.nlm.nih.gov/pubmed/26075713
http://dx.doi.org/10.1371/journal.pone.0128540
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