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Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

BACKGROUND: It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. OBJECTIVE: The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. METHODS: Based on...

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Detalles Bibliográficos
Autores principales: Guo, Lanwei, Meng, Qingcheng, Zheng, Liyang, Chen, Qiong, Liu, Yin, Xu, Huifang, Kang, Ruihua, Zhang, Luyao, Liu, Shuzheng, Sun, Xibin, Zhang, Shaokai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862335/
https://www.ncbi.nlm.nih.gov/pubmed/36607729
http://dx.doi.org/10.2196/41640
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author Guo, Lanwei
Meng, Qingcheng
Zheng, Liyang
Chen, Qiong
Liu, Yin
Xu, Huifang
Kang, Ruihua
Zhang, Luyao
Liu, Shuzheng
Sun, Xibin
Zhang, Shaokai
author_facet Guo, Lanwei
Meng, Qingcheng
Zheng, Liyang
Chen, Qiong
Liu, Yin
Xu, Huifang
Kang, Ruihua
Zhang, Luyao
Liu, Shuzheng
Sun, Xibin
Zhang, Shaokai
author_sort Guo, Lanwei
collection PubMed
description BACKGROUND: It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. OBJECTIVE: The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. METHODS: Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. RESULTS: In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk–predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. CONCLUSIONS: We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.
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spelling pubmed-98623352023-01-22 Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study Guo, Lanwei Meng, Qingcheng Zheng, Liyang Chen, Qiong Liu, Yin Xu, Huifang Kang, Ruihua Zhang, Luyao Liu, Shuzheng Sun, Xibin Zhang, Shaokai JMIR Public Health Surveill Original Paper BACKGROUND: It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. OBJECTIVE: The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. METHODS: Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. RESULTS: In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk–predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. CONCLUSIONS: We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women. JMIR Publications 2023-01-06 /pmc/articles/PMC9862335/ /pubmed/36607729 http://dx.doi.org/10.2196/41640 Text en ©Lanwei Guo, Qingcheng Meng, Liyang Zheng, Qiong Chen, Yin Liu, Huifang Xu, Ruihua Kang, Luyao Zhang, Shuzheng Liu, Xibin Sun, Shaokai Zhang. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 06.01.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Guo, Lanwei
Meng, Qingcheng
Zheng, Liyang
Chen, Qiong
Liu, Yin
Xu, Huifang
Kang, Ruihua
Zhang, Luyao
Liu, Shuzheng
Sun, Xibin
Zhang, Shaokai
Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study
title Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study
title_full Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study
title_fullStr Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study
title_full_unstemmed Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study
title_short Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study
title_sort lung cancer risk prediction nomogram in nonsmoking chinese women: retrospective cross-sectional cohort study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9862335/
https://www.ncbi.nlm.nih.gov/pubmed/36607729
http://dx.doi.org/10.2196/41640
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