Risk assessment and prediction for lung cancer among Hong Kong Chinese men

Abstract Objective Most of the previous risk prediction models for lung cancer were developed from smokers, with discriminatory power ranging from 0.57 to 0.72. We constructed an individual risk prediction model for lung cancer among the male general population of Hong Kong. Methods Epidemiological...

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Main Authors: Lap Ah Tse, Feng Wang, Martin Chi-sang Wong, Joseph Siu-kei Au, Ignatius Tak-sun Yu
Format: Article
Language:English
Published: BMC 2022-05-01
Series:BMC Cancer
Subjects:
Online Access:https://doi.org/10.1186/s12885-022-09678-y
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author Lap Ah Tse
Feng Wang
Martin Chi-sang Wong
Joseph Siu-kei Au
Ignatius Tak-sun Yu
author_facet Lap Ah Tse
Feng Wang
Martin Chi-sang Wong
Joseph Siu-kei Au
Ignatius Tak-sun Yu
author_sort Lap Ah Tse
collection DOAJ
description Abstract Objective Most of the previous risk prediction models for lung cancer were developed from smokers, with discriminatory power ranging from 0.57 to 0.72. We constructed an individual risk prediction model for lung cancer among the male general population of Hong Kong. Methods Epidemiological data of 1,069 histology confirmed male lung cancer cases and 1,208 community controls were included in this analysis. Residential radon exposure was retrospectively reconstructed based on individual lifetime residential information. Multivariable logistic regression with repeated cross-validation method was used to select optimal risk predictors for each prediction model for different smoking strata. Individual absolute risk for lung cancer was estimated by Gail model. Receiver-operator characteristic curves, area under the curve (AUC) and confusion matrix were evaluated to demonstrate the model performance and ability to differentiate cases from non-cases. Results Smoking and smoking cessation, education, lung disease history, family history of cancer, residential radon exposure, dietary habits, carcinogens exposure, mask use and dust control in workplace were selected as the risk predictors for lung cancer. The AUC of estimated absolute risk for all lung cancers was 0.735 (95% CI: 0.714–0.756). Using 2.83% as the cutoff point of absolute risk, the predictive accuracy, positive predictive value and negative predictive value were 0.715, 0.818 and 0.674, respectively. Conclusion We developed a risk prediction model with moderate discrimination for lung cancer among Hong Kong males. External validation in other populations is warranted for this model in future studies.
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spelling doaj.art-74980132943c41b0825ac083e2c722082022-12-22T00:35:10ZengBMCBMC Cancer1471-24072022-05-012211910.1186/s12885-022-09678-yRisk assessment and prediction for lung cancer among Hong Kong Chinese menLap Ah Tse0Feng Wang1Martin Chi-sang Wong2Joseph Siu-kei Au3Ignatius Tak-sun Yu4JC School of Public Health and Primary Care, The Chinese University of Hong KongJC School of Public Health and Primary Care, The Chinese University of Hong KongJC School of Public Health and Primary Care, The Chinese University of Hong KongDepartment of Clinical Oncology, Hong Kong Adventist HospitalJC School of Public Health and Primary Care, The Chinese University of Hong KongAbstract Objective Most of the previous risk prediction models for lung cancer were developed from smokers, with discriminatory power ranging from 0.57 to 0.72. We constructed an individual risk prediction model for lung cancer among the male general population of Hong Kong. Methods Epidemiological data of 1,069 histology confirmed male lung cancer cases and 1,208 community controls were included in this analysis. Residential radon exposure was retrospectively reconstructed based on individual lifetime residential information. Multivariable logistic regression with repeated cross-validation method was used to select optimal risk predictors for each prediction model for different smoking strata. Individual absolute risk for lung cancer was estimated by Gail model. Receiver-operator characteristic curves, area under the curve (AUC) and confusion matrix were evaluated to demonstrate the model performance and ability to differentiate cases from non-cases. Results Smoking and smoking cessation, education, lung disease history, family history of cancer, residential radon exposure, dietary habits, carcinogens exposure, mask use and dust control in workplace were selected as the risk predictors for lung cancer. The AUC of estimated absolute risk for all lung cancers was 0.735 (95% CI: 0.714–0.756). Using 2.83% as the cutoff point of absolute risk, the predictive accuracy, positive predictive value and negative predictive value were 0.715, 0.818 and 0.674, respectively. Conclusion We developed a risk prediction model with moderate discrimination for lung cancer among Hong Kong males. External validation in other populations is warranted for this model in future studies.https://doi.org/10.1186/s12885-022-09678-yRisk predictionLung cancerResidential radon exposureIndoor air pollution
spellingShingle Lap Ah Tse
Feng Wang
Martin Chi-sang Wong
Joseph Siu-kei Au
Ignatius Tak-sun Yu
Risk assessment and prediction for lung cancer among Hong Kong Chinese men
BMC Cancer
Risk prediction
Lung cancer
Residential radon exposure
Indoor air pollution
title Risk assessment and prediction for lung cancer among Hong Kong Chinese men
title_full Risk assessment and prediction for lung cancer among Hong Kong Chinese men
title_fullStr Risk assessment and prediction for lung cancer among Hong Kong Chinese men
title_full_unstemmed Risk assessment and prediction for lung cancer among Hong Kong Chinese men
title_short Risk assessment and prediction for lung cancer among Hong Kong Chinese men
title_sort risk assessment and prediction for lung cancer among hong kong chinese men
topic Risk prediction
Lung cancer
Residential radon exposure
Indoor air pollution
url https://doi.org/10.1186/s12885-022-09678-y
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