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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...

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Autores principales: Wang, Yiyang, Zheng, Difan, Luo, Jizhuang, Zhang, Jie, Pompili, Cecilia, Ujiie, Hideki, Matsuura, Natsumi, Chen, Haiquan, Yao, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
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.
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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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