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A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm

We sought to develop and validate a clinical nomogram model for predicting overall survival (OS) in non-small-cell lung cancer (NSCLC) patients with resected tumors that were 30 mm or smaller, using clinical data and molecular marker findings. We retrospectively analyzed 786 NSCLC patients with a pa...

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Autores principales: Zhu, Wang-Yu, Fang, Ke-xin, He, Jian-ying, Cui, Ri, Zhang, Yong-Kui, Le, Han-bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525952/
https://www.ncbi.nlm.nih.gov/pubmed/31191756
http://dx.doi.org/10.1155/2019/8435893
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author Zhu, Wang-Yu
Fang, Ke-xin
He, Jian-ying
Cui, Ri
Zhang, Yong-Kui
Le, Han-bo
author_facet Zhu, Wang-Yu
Fang, Ke-xin
He, Jian-ying
Cui, Ri
Zhang, Yong-Kui
Le, Han-bo
author_sort Zhu, Wang-Yu
collection PubMed
description We sought to develop and validate a clinical nomogram model for predicting overall survival (OS) in non-small-cell lung cancer (NSCLC) patients with resected tumors that were 30 mm or smaller, using clinical data and molecular marker findings. We retrospectively analyzed 786 NSCLC patients with a pathological tumor size less than 30 mm who underwent surgery between 2007 and 2017 at our institution. We identified and integrated significant prognostic factors to build the nomogram model using the training set, which was subjected to the internal data validation. The prognostic performance was calibrated and evaluated by the concordance index (C-index) and risk group stratification. Multivariable analysis identified the pathological tumor size, lymph node metastasis, and Ki-67 expression as independent prognostic factors, which were entered into the nomogram model. The nomogram-predicted probabilities of OS at 1 year, 3 years, and 5 years posttreatment represented optimal concordance with the actual observations. Harrell's C-index of the constructed nomogram with the training set was 0.856 (95% CI: 0.804-0.908), whereas TNM staging was 0.814 (95% CI: 0.742-0.886, P = 5.280221e − 13). Survival analysis demonstrated that NSCLC subgroups showed significant differences in the training and validation sets (P < 0.001). A nomogram model was established for predicting survival in NSCLC patients with a pathological tumor size less than 30 mm, which would be further validated using demographic and clinicopathological data. In the future, this prognostic model may assist clinicians during treatment planning and clinical studies.
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spelling pubmed-65259522019-06-12 A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm Zhu, Wang-Yu Fang, Ke-xin He, Jian-ying Cui, Ri Zhang, Yong-Kui Le, Han-bo Dis Markers Research Article We sought to develop and validate a clinical nomogram model for predicting overall survival (OS) in non-small-cell lung cancer (NSCLC) patients with resected tumors that were 30 mm or smaller, using clinical data and molecular marker findings. We retrospectively analyzed 786 NSCLC patients with a pathological tumor size less than 30 mm who underwent surgery between 2007 and 2017 at our institution. We identified and integrated significant prognostic factors to build the nomogram model using the training set, which was subjected to the internal data validation. The prognostic performance was calibrated and evaluated by the concordance index (C-index) and risk group stratification. Multivariable analysis identified the pathological tumor size, lymph node metastasis, and Ki-67 expression as independent prognostic factors, which were entered into the nomogram model. The nomogram-predicted probabilities of OS at 1 year, 3 years, and 5 years posttreatment represented optimal concordance with the actual observations. Harrell's C-index of the constructed nomogram with the training set was 0.856 (95% CI: 0.804-0.908), whereas TNM staging was 0.814 (95% CI: 0.742-0.886, P = 5.280221e − 13). Survival analysis demonstrated that NSCLC subgroups showed significant differences in the training and validation sets (P < 0.001). A nomogram model was established for predicting survival in NSCLC patients with a pathological tumor size less than 30 mm, which would be further validated using demographic and clinicopathological data. In the future, this prognostic model may assist clinicians during treatment planning and clinical studies. Hindawi 2019-05-02 /pmc/articles/PMC6525952/ /pubmed/31191756 http://dx.doi.org/10.1155/2019/8435893 Text en Copyright © 2019 Wang-Yu Zhu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhu, Wang-Yu
Fang, Ke-xin
He, Jian-ying
Cui, Ri
Zhang, Yong-Kui
Le, Han-bo
A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm
title A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm
title_full A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm
title_fullStr A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm
title_full_unstemmed A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm
title_short A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm
title_sort prediction rule for overall survival in non-small-cell lung cancer patients with a pathological tumor size less than 30 mm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525952/
https://www.ncbi.nlm.nih.gov/pubmed/31191756
http://dx.doi.org/10.1155/2019/8435893
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