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Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer
Serum enzymes, blood cytology indices, and pathological features are associated with the prognosis of patients with lung cancer, and we construct prognostic prediction models based on clinicopathological indices in patients with resectable lung cancer. The study includes 420 patients with primary lu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658583/ https://www.ncbi.nlm.nih.gov/pubmed/33194667 http://dx.doi.org/10.3389/fonc.2020.571169 |
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author | Liu, Yanyan Li, Xinying Yin, Zhucheng Lu, Ping Ma, Yifei Kai, Jindan Luo, Bo Wei, Shaozhong Liang, Xinjun |
author_facet | Liu, Yanyan Li, Xinying Yin, Zhucheng Lu, Ping Ma, Yifei Kai, Jindan Luo, Bo Wei, Shaozhong Liang, Xinjun |
author_sort | Liu, Yanyan |
collection | PubMed |
description | Serum enzymes, blood cytology indices, and pathological features are associated with the prognosis of patients with lung cancer, and we construct prognostic prediction models based on clinicopathological indices in patients with resectable lung cancer. The study includes 420 patients with primary lung cancer who underwent pneumonectomy. Cox proportional hazards regression was conducted to analyze the prognostic values of individual clinicopathological indices. The prediction accuracies of models for overall survival (OS) and progression-free survival (PFS) were estimated through Harrell’s concordance indices (C-index) and Brier scores. Nomograms of the prognostic models were plotted for individualized evaluations of death and cancer progression. We find that the prognostic model based on alkaline phosphatase (ALP), lactate dehydrogenase (LDH), age, history of tuberculosis, and pathological stage present exceptional performance for OS prediction [C-index: 0.74 (95% CI, 0.69-0.79) and Brier score: 0.10], and the prognostic model based on ALP, LDH, and platelet distribution width (PDW), age, pathological stage, and histological type presented outstanding performance for PFS prediction [C-index: 0.71 (95% CI, 0.66-0.75) and Brier score: 0.18]. These findings show that the models based on clinicopathological indices might serve as economic and efficient prognostic tools for resectable lung cancer. |
format | Online Article Text |
id | pubmed-7658583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76585832020-11-13 Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer Liu, Yanyan Li, Xinying Yin, Zhucheng Lu, Ping Ma, Yifei Kai, Jindan Luo, Bo Wei, Shaozhong Liang, Xinjun Front Oncol Oncology Serum enzymes, blood cytology indices, and pathological features are associated with the prognosis of patients with lung cancer, and we construct prognostic prediction models based on clinicopathological indices in patients with resectable lung cancer. The study includes 420 patients with primary lung cancer who underwent pneumonectomy. Cox proportional hazards regression was conducted to analyze the prognostic values of individual clinicopathological indices. The prediction accuracies of models for overall survival (OS) and progression-free survival (PFS) were estimated through Harrell’s concordance indices (C-index) and Brier scores. Nomograms of the prognostic models were plotted for individualized evaluations of death and cancer progression. We find that the prognostic model based on alkaline phosphatase (ALP), lactate dehydrogenase (LDH), age, history of tuberculosis, and pathological stage present exceptional performance for OS prediction [C-index: 0.74 (95% CI, 0.69-0.79) and Brier score: 0.10], and the prognostic model based on ALP, LDH, and platelet distribution width (PDW), age, pathological stage, and histological type presented outstanding performance for PFS prediction [C-index: 0.71 (95% CI, 0.66-0.75) and Brier score: 0.18]. These findings show that the models based on clinicopathological indices might serve as economic and efficient prognostic tools for resectable lung cancer. Frontiers Media S.A. 2020-10-29 /pmc/articles/PMC7658583/ /pubmed/33194667 http://dx.doi.org/10.3389/fonc.2020.571169 Text en Copyright © 2020 Liu, Li, Yin, Lu, Ma, Kai, Luo, Wei and Liang http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Liu, Yanyan Li, Xinying Yin, Zhucheng Lu, Ping Ma, Yifei Kai, Jindan Luo, Bo Wei, Shaozhong Liang, Xinjun Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer |
title | Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer |
title_full | Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer |
title_fullStr | Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer |
title_full_unstemmed | Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer |
title_short | Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer |
title_sort | prognostic prediction models based on clinicopathological indices in patients with resectable lung cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658583/ https://www.ncbi.nlm.nih.gov/pubmed/33194667 http://dx.doi.org/10.3389/fonc.2020.571169 |
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