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Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors
BACKGROUND: The aim of this study was to propose a new kind of pathological classification and further establish a prognostic model for resected stage I invasive adenocarcinoma (IADC). METHODS: Clinicopathological data were collected from 2 hospitals. The new proposed pathological reclassification w...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182714/ https://www.ncbi.nlm.nih.gov/pubmed/34164270 http://dx.doi.org/10.21037/tlcr-21-393 |
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author | Wang, Yiyang Zheng, Difan Luo, Jizhuang Zhang, Jie Pompili, Cecilia Ujiie, Hideki Matsuura, Natsumi Chen, Haiquan Yao, Feng |
author_facet | Wang, Yiyang Zheng, Difan Luo, Jizhuang Zhang, Jie Pompili, Cecilia Ujiie, Hideki Matsuura, Natsumi Chen, Haiquan Yao, Feng |
author_sort | Wang, Yiyang |
collection | PubMed |
description | BACKGROUND: The aim of this study was to propose a new kind of pathological classification and further establish a prognostic model for resected stage I invasive adenocarcinoma (IADC). METHODS: Clinicopathological data were collected from 2 hospitals. The new proposed pathological reclassification was defined according to certain subtype instead of a predominant one. Survival curves were plotted by Kaplan-Meier analysis. Cox regressions were analyzed for recurrence-free survival (RFS) and overall survival (OS), through which prognostic scores and stratification models were established. The comparison between risk models and the eighth edition of tumor, node, metastasis (TNM) classification was conducted through receiver operating characteristic curves (ROC), as identified by the area under the curve (AUC) and z test. RESULTS: In all, 1,196 patients were enrolled. At multivariable analysis, solid and micropapillary of the new pathological reclassification, along with stage IA3 and IB were independent predictors for poorer RFS. Stage IB and smoking status significantly indicated worse OS. After normalization and standardization of log-hazard ratio (HR), personalized scores were calculated and the risk stratifications with 3 risk groups were generated. Compared with TNM classification, the risk model of RFS showed advantage over early-recurrence prediction (1-year: 0.653 vs. 0.556, P=0.033; 3-year: 0.663 vs. 0.076, P=0.008). No marked difference was observed in long-term RFS or OS. CONCLUSIONS: Considering the harboring of certain patterns may be a new concept in adenocarcinoma classification. The risk stratification model based on this pathological classification and the eighth TNM classification showed remarkable superiority over TNM alone in predicting early recurrence of stage I adenocarcinoma. However, TNM classification remained valuable for long-term recurrence and survival prediction. |
format | Online Article Text |
id | pubmed-8182714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-81827142021-06-22 Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors Wang, Yiyang Zheng, Difan Luo, Jizhuang Zhang, Jie Pompili, Cecilia Ujiie, Hideki Matsuura, Natsumi Chen, Haiquan Yao, Feng Transl Lung Cancer Res Original Article BACKGROUND: The aim of this study was to propose a new kind of pathological classification and further establish a prognostic model for resected stage I invasive adenocarcinoma (IADC). METHODS: Clinicopathological data were collected from 2 hospitals. The new proposed pathological reclassification was defined according to certain subtype instead of a predominant one. Survival curves were plotted by Kaplan-Meier analysis. Cox regressions were analyzed for recurrence-free survival (RFS) and overall survival (OS), through which prognostic scores and stratification models were established. The comparison between risk models and the eighth edition of tumor, node, metastasis (TNM) classification was conducted through receiver operating characteristic curves (ROC), as identified by the area under the curve (AUC) and z test. RESULTS: In all, 1,196 patients were enrolled. At multivariable analysis, solid and micropapillary of the new pathological reclassification, along with stage IA3 and IB were independent predictors for poorer RFS. Stage IB and smoking status significantly indicated worse OS. After normalization and standardization of log-hazard ratio (HR), personalized scores were calculated and the risk stratifications with 3 risk groups were generated. Compared with TNM classification, the risk model of RFS showed advantage over early-recurrence prediction (1-year: 0.653 vs. 0.556, P=0.033; 3-year: 0.663 vs. 0.076, P=0.008). No marked difference was observed in long-term RFS or OS. CONCLUSIONS: Considering the harboring of certain patterns may be a new concept in adenocarcinoma classification. The risk stratification model based on this pathological classification and the eighth TNM classification showed remarkable superiority over TNM alone in predicting early recurrence of stage I adenocarcinoma. However, TNM classification remained valuable for long-term recurrence and survival prediction. AME Publishing Company 2021-05 /pmc/articles/PMC8182714/ /pubmed/34164270 http://dx.doi.org/10.21037/tlcr-21-393 Text en 2021 Translational Lung Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Wang, Yiyang Zheng, Difan Luo, Jizhuang Zhang, Jie Pompili, Cecilia Ujiie, Hideki Matsuura, Natsumi Chen, Haiquan Yao, Feng Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors |
title | Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors |
title_full | Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors |
title_fullStr | Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors |
title_full_unstemmed | Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors |
title_short | Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors |
title_sort | risk stratification model for patients with stage i invasive lung adenocarcinoma based on clinical and pathological predictors |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182714/ https://www.ncbi.nlm.nih.gov/pubmed/34164270 http://dx.doi.org/10.21037/tlcr-21-393 |
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