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陈 睿, 王 静, 王 硕, 唐 思, 索 晨. [Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:892-898. [PMID: 37866943 PMCID: PMC10579072 DOI: 10.12182/20230960209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Indexed: 10/24/2023]
Abstract
Objective To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer. Methods The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated. Results The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population. Conclusion We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.
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Affiliation(s)
- 睿琳 陈
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 静茹 王
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 硕 王
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 思琦 唐
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 晨 索
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
- 上海市重大传染病和生物安全研究院 (上海 200032)Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China
- 复旦大学泰州健康科学研究院 (泰州 225316)Fudan University Taizhou Institute of Health Sciences, Taizhou 225316, China
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Ma Z, Lv J, Zhu M, Yu C, Ma H, Jin G, Guo Y, Bian Z, Yang L, Chen Y, Chen Z, Hu Z, Li L, Shen H. Lung cancer risk score for ever and never smokers in China. Cancer Commun (Lond) 2023; 43:877-895. [PMID: 37410540 PMCID: PMC10397566 DOI: 10.1002/cac2.12463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/23/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Most lung cancer risk prediction models were developed in European and North-American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range. METHODS Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow-up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers. RESULTS A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon-linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6-year area under the receiver operating curve for the ever and never smokers' models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10-year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate-high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10-year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS. CONCLUSIONS The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.
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Affiliation(s)
- Zhimin Ma
- Department of EpidemiologyCenter for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer BiomarkersPrevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsuP. R. China
- Department of EpidemiologySchool of Public HealthSoutheast UniversityNanjingJiangsuP. R. China
| | - Jun Lv
- Department of Epidemiology & BiostatisticsSchool of Public HealthPeking UniversityBeijingP. R. China
- Ministry of EducationKey Laboratory of Molecular Cardiovascular Sciences (Peking University)BeijingP. R. China
| | - Meng Zhu
- Department of EpidemiologyCenter for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer BiomarkersPrevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Canqing Yu
- Department of Epidemiology & BiostatisticsSchool of Public HealthPeking UniversityBeijingP. R. China
| | - Hongxia Ma
- Department of EpidemiologyCenter for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer BiomarkersPrevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Guangfu Jin
- Department of EpidemiologyCenter for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer BiomarkersPrevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Yu Guo
- Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zheng Bian
- Chinese Academy of Medical SciencesBeijingP. R. China
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU)Nuffield Department of Population HealthUniversity of OxfordOxfordOxfordshireUK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU)Nuffield Department of Population HealthUniversity of OxfordOxfordOxfordshireUK
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU)Nuffield Department of Population HealthUniversity of OxfordOxfordOxfordshireUK
| | - Zhibin Hu
- Department of EpidemiologyCenter for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer BiomarkersPrevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsuP. R. China
| | - Liming Li
- Department of Epidemiology & BiostatisticsSchool of Public HealthPeking UniversityBeijingP. R. China
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU)Nuffield Department of Population HealthUniversity of OxfordOxfordOxfordshireUK
| | - Hongbing Shen
- Department of EpidemiologyCenter for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingJiangsuP. R. China
- Jiangsu Key Lab of Cancer BiomarkersPrevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsuP. R. China
- Research Units of Cohort Study on Cardiovascular Diseases and CancersChinese Academy of Medical SciencesBeijingP. R. China
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Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H, Kang R, Zhang L, Liu S, Sun X, Zhang S. Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study. JMIR Public Health Surveill 2023; 9:e41640. [PMID: 36607729 PMCID: PMC9862335 DOI: 10.2196/41640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 01/07/2023] Open
Abstract
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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Affiliation(s)
- Lanwei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Liyang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Huifang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Ruihua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Luyao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shuzheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Xibin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shaokai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
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Yuan J, Sun Y, Xu F, Li M, Fan M, Zhang C, Wang K, Li H, Bu X, Yan X, Wang J, Ma J, Zhang G, Chen M, Ren H. Cost-effectiveness of lung cancer screening combined with nurse-led smoking cessation intervention: A population-based microsimulation study. Int J Nurs Stud 2022; 134:104319. [DOI: 10.1016/j.ijnurstu.2022.104319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/17/2022] [Accepted: 06/25/2022] [Indexed: 10/17/2022]
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Age-dependent genomic characteristics and their impact on immunotherapy in lung adenocarcinoma. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04195-8. [PMID: 35838838 DOI: 10.1007/s00432-022-04195-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/06/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND The incidence of lung cancer tends to be younger, and adenocarcinoma is the main histological type. Even patients with the same tumor type may have significant differences in clinical features, tumor microenvironment and genomic background at different ages. Immune checkpoint inhibitors (ICIs) have been shown to improve clinical outcomes in patients with lung adenocarcinoma (LUAD). However, differences in ICI efficacy between older and younger patients are unknown. Our study aimed to explore the relationship between age and immunotherapy in LUAD. METHODS In our study, 1313 resected LUAD patients in our hospital were divided into young (age ≤ 50) and old groups (age > 50), and the clinical characteristic differences between them were analyzed. Of these, next-generation sequencing (NGS) was performed on the 311 cases. In addition, immune-related signatures of 508 LUAD patients were analyzed by TCGA RNA expression data. Then, we validated genomic and clinical information of 270 LUAD samples in the MSKCC cohort. RESULTS ERBB2 and EGFR gene mutations were significantly different between the two groups, and the gene mutation number in the old group was significantly higher than that in the young group. In addition, immune-related signatures of LUAD patients were analyzed by TCGA RNA expression data, which indicated that the patients in the old group might have a better immune microenvironment. Then, we validated the MSKCC cohort and found that the TMB of the old group was significantly higher than that of the young group, and the OS of immunotherapy was longer in the old group. CONCLUSION Our study was the first to analyze the differences in the genomic landscape and immune-related biomarkers between the young and old groups of LUAD patients and found that the old group had a better efficacy of immunotherapy, providing a reference for the study design and treatment of patients with LUAD.
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China. Front Oncol 2022; 11:766939. [PMID: 35059311 PMCID: PMC8764453 DOI: 10.3389/fonc.2021.766939] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China. Methods A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. Results A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk. Conclusions We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Shao-Kai Zhang,
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China. Lung Cancer 2021; 163:27-34. [PMID: 34894456 DOI: 10.1016/j.lungcan.2021.11.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer. MATERIALS AND METHODS Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively. RESULTS By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. CONCLUSIONS We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China.
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Schlage WK, Titz B, Iskandar A, Poussin C, Van der Toorn M, Wong ET, Pratte P, Maeder S, Schaller JP, Pospisil P, Boue S, Vuillaume G, Leroy P, Martin F, Ivanov NV, Peitsch MC, Hoeng J. Comparing the preclinical risk profile of inhalable candidate and potential candidate modified risk tobacco products: A bridging use case. Toxicol Rep 2020; 7:1187-1206. [PMID: 32995294 PMCID: PMC7502378 DOI: 10.1016/j.toxrep.2020.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 06/24/2020] [Accepted: 09/01/2020] [Indexed: 11/25/2022] Open
Abstract
Heated tobacco products tested for reduced exposure and reduced risk properties. Bridging opportunities for nonclinical results from two heated tobacco products. Similarly reduced impact on apical and molecular endpoints relative to cigarettes. Evidence evaluated along a “causal chain of events leading to disease” (CELSD). Representative assays along CELSD could support nonclinical substantial equivalence.
Cigarette smoking causes major preventable diseases, morbidity, and mortality worldwide. Smoking cessation and prevention of smoking initiation are the preferred means for reducing these risks. Less harmful tobacco products, termed modified-risk tobacco products (MRTP), are being developed as a potential alternative for current adult smokers who would otherwise continue smoking. According to a regulatory framework issued by the US Food and Drug Administration, a manufacturer must provide comprehensive scientific evidence that the product significantly reduces harm and the risk of tobacco-related diseases, in order to obtain marketing authorization for a new MRTP. For new tobacco products similar to an already approved predicate product, the FDA has foreseen a simplified procedure for assessing “substantial equivalence”. In this article, we present a use case that bridges the nonclinical evidence from previous studies demonstrating the relatively reduced harm potential of two heat-not-burn products based on different tobacco heating principles. The nonclinical evidence was collected along a “causal chain of events leading to disease” (CELSD) to systematically follow the consequences of reduced exposure to toxicants (relative to cigarette smoke) through increasing levels of biological complexity up to disease manifestation in animal models of human disease. This approach leverages the principles of systems biology and toxicology as a basis for further extrapolation to human studies. The experimental results demonstrate a similarly reduced impact of both products on apical and molecular endpoints, no novel effects not seen with cigarette smoke exposure, and an effect of switching from cigarettes to either MRTP that is comparable to that of complete smoking cessation. Ideally, a subset of representative assays from the presented sequence along the CELSD could be sufficient for predicting similarity or substantial equivalence in the nonclinical impact of novel products; this would require further validation, for which the present use case could serve as a starting point.
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Key Words
- BIF, biological impact factor
- CELSD, causal chain of events leading to disease
- CHTP, carbon heated tobacco product
- CS, cigarette smoke
- CVD, cardiovascular disease
- GVP, gas/vapor phase
- HPHC, harmful and potentially harmful constituents
- MRTP, modified risk tobacco product
- Modified risk tobacco product (MRTP)
- NPA, network perturbation amplitude
- PMI, Philip Morris International
- RBIF, relative BIF
- Substantial equivalence
- Systems toxicology
- THS, Tobacco Heating System
- TPM, total particulate matter
- Tobacco harm reduction
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Affiliation(s)
| | - Bjoern Titz
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Anita Iskandar
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Carine Poussin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Marco Van der Toorn
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Ee Tsin Wong
- PMI R&D, Philip Morris International Research Laboratories Pte. Ltd., Science Park II, Singapore
| | - Pascal Pratte
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Serge Maeder
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Jean-Pierre Schaller
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Pavel Pospisil
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Stephanie Boue
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Grégory Vuillaume
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Patrice Leroy
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Florian Martin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Nikolai V Ivanov
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
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9
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Hori M, Saito E, Katanoda K, Tsugane S. Estimation of lifetime cumulative mortality risk of lung cancer by smoking status in Japan. Jpn J Clin Oncol 2020; 50:1218-1224. [DOI: 10.1093/jjco/hyaa094] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 06/10/2020] [Indexed: 11/14/2022] Open
Abstract
Abstract
The main objective of this study was to estimate the cumulative mortality risk for lung cancer according to smoking status and exposure to secondhand smoke (SHS) in Japan. We applied a life-table method to estimate the cumulative risk. Estimated lifetime cumulative risk for 20-year-old non-smokers, former, and current smokers was 3.2%, 7.6%, and 14.9% for men and 1.9%, 5.6%, and 7.2% for women, respectively. For males, the estimated risk was 3.2% for non-smokers not exposed to SHS and 4.1% for non-smokers exposed to SHS. For females, the estimated risk was 1.9% for non-smokers not exposed to SHS and 2.4% for non-smokers exposed to SHS. Lifetime cumulative mortality risk differed greatly according to smoking status. Moreover, SHS exposure resulted in observable differences in lifetime mortality risk. This study may be useful for the development of tailored prevention programs.
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Affiliation(s)
- Megumi Hori
- Division of Cancer Statistics Integration, Center for Cancer Control and Information Services, National Cancer Center, Tokyo, Japan
| | - Eiko Saito
- Division of Cancer Statistics Integration, Center for Cancer Control and Information Services, National Cancer Center, Tokyo, Japan
| | - Kota Katanoda
- Division of Cancer Statistics Integration, Center for Cancer Control and Information Services, National Cancer Center, Tokyo, Japan
| | - Shoichiro Tsugane
- Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
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10
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Lyu Z, Li N, Chen S, Wang G, Tan F, Feng X, Li X, Wen Y, Yang Z, Wang Y, Li J, Chen H, Lin C, Ren J, Shi J, Wu S, Dai M, He J. Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population. Cancer Med 2020; 9:3983-3994. [PMID: 32253829 PMCID: PMC7286442 DOI: 10.1002/cam4.3025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/20/2020] [Accepted: 03/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population. METHODS During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with Pentry = .15 and Pstay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability. RESULTS A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (PHL = .689) and all subgroups. CONCLUSIONS We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.
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Affiliation(s)
- Zhangyan Lyu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuohua Chen
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Gang Wang
- Health Department of Kailuan (Group), Tangshan, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoshuang Feng
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Wen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuoyu Yang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yalong Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongda Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunqing Lin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiansong Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jufang Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shouling Wu
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Min Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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11
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Wang X, Zhang Y, Hao S, Zheng L, Liao J, Ye C, Xia M, Wang O, Liu M, Weng CH, Duong SQ, Jin B, Alfreds ST, Stearns F, Kanov L, Sylvester KG, Widen E, McElhinney DB, Ling XB. Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine. J Med Internet Res 2019; 21:e13260. [PMID: 31099339 PMCID: PMC6542253 DOI: 10.2196/13260] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 04/18/2019] [Accepted: 04/23/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. OBJECTIVE The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population. METHODS Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. RESULTS The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. CONCLUSIONS We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.
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Affiliation(s)
- Xiaofang Wang
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China.,Department of Surgery, Stanford University, Stanford, CA, United States
| | - Yan Zhang
- Department of Oncology, The First Hospital of Shijiazhuang, Shijiazhuang, China
| | - Shiying Hao
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
| | - Le Zheng
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
| | - Jiayu Liao
- Department of Bioengineering, University of California, Riverside, CA, United States.,West China-California Multiomics Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Chengyin Ye
- Department of Health Management, Hangzhou Normal University, Hangzhou, China
| | - Minjie Xia
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | - Oliver Wang
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | - Modi Liu
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | - Ching Ho Weng
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Son Q Duong
- Lucile Packard Children's Hospital, Palo Alto, CA, United States
| | - Bo Jin
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | | | - Frank Stearns
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | - Laura Kanov
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | - Karl G Sylvester
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Eric Widen
- Healthcare Business Intelligence Solutions Inc, Palo Alto, CA, United States
| | - Doff B McElhinney
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
| | - Xuefeng B Ling
- Department of Surgery, Stanford University, Stanford, CA, United States.,Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States
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12
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Yamaguchi T, Nishiura H. Predicting the Epidemiological Dynamics of Lung Cancer in Japan. J Clin Med 2019; 8:jcm8030326. [PMID: 30857126 PMCID: PMC6463119 DOI: 10.3390/jcm8030326] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 02/19/2019] [Accepted: 03/05/2019] [Indexed: 12/17/2022] Open
Abstract
While the prevalence of smoking has steadily declined over time, the absolute numbers of lung cancer cases and deaths have continued to increase in Japan. We employed a simple mathematical model that describes the relationship between demographic dynamics and smoking prevalence to predict future epidemiological trends of lung cancer by age and sex. Never-smokers, smokers, and ex-smokers were assumed to experience different hazard of lung cancer, and the model was parameterized using data from 2014 and before, as learning data, and a future forecast was obtained from 2015 onwards. The maximum numbers of lung cancer cases and deaths in men will be 76,978 (95% confidence interval (CI): 76,630⁻77,253) and 63,284 (95% CI: 62,991⁻63507) in 2024, while those in women will be 42,838 (95% CI: 42,601⁻43,095) and 32,267 (95% CI: 32,063⁻32,460) in 2035 and 2036, respectively. Afterwards, the absolute numbers of cases and deaths are predicted to decrease monotonically. Our compartmental modeling approach is well suited to predicting lung cancer in Japan with dynamic ageing and drastic decline in smoking prevalence. The predicted burden is useful for anticipating demands for diagnosis, treatment, and care in the healthcare sector.
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Affiliation(s)
- Takayuki Yamaguchi
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan.
- CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama 332-0012, Japan.
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan.
- CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama 332-0012, Japan.
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13
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Charvat H, Sasazuki S, Shimazu T, Budhathoki S, Inoue M, Iwasaki M, Sawada N, Yamaji T, Tsugane S. Development of a risk prediction model for lung cancer: The Japan Public Health Center-based Prospective Study. Cancer Sci 2018; 109:854-862. [PMID: 29345859 PMCID: PMC5834815 DOI: 10.1111/cas.13509] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/28/2017] [Accepted: 01/08/2018] [Indexed: 02/02/2023] Open
Abstract
Although the impact of tobacco consumption on the occurrence of lung cancer is well‐established, risk estimation could be improved by risk prediction models that consider various smoking habits, such as quantity, duration, and time since quitting. We constructed a risk prediction model using a population of 59 161 individuals from the Japan Public Health Center (JPHC) Study Cohort II. A parametric survival model was used to assess the impact of age, gender, and smoking‐related factors (cumulative smoking intensity measured in pack‐years, age at initiation, and time since cessation). Ten‐year cumulative probability of lung cancer occurrence estimates were calculated with consideration of the competing risk of death from other causes. Finally, the model was externally validated using 47 501 individuals from JPHC Study Cohort I. A total of 1210 cases of lung cancer occurred during 986 408 person‐years of follow‐up. We found a dose‐dependent effect of tobacco consumption with hazard ratios for current smokers ranging from 3.78 (2.00‐7.16) for cumulative consumption ≤15 pack‐years to 15.80 (9.67‐25.79) for >75 pack‐years. Risk decreased with time since cessation. Ten‐year cumulative probability of lung cancer occurrence estimates ranged from 0.04% to 11.14% in men and 0.07% to 6.55% in women. The model showed good predictive performance regarding discrimination (cross‐validated c‐index = 0.793) and calibration (cross‐validated χ2 = 6.60; P‐value = .58). The model still showed good discrimination in the external validation population (c‐index = 0.772). In conclusion, we developed a prediction model to estimate the probability of developing lung cancer based on age, gender, and tobacco consumption. This model appears useful in encouraging high‐risk individuals to quit smoking and undergo increased surveillance.
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Affiliation(s)
- Hadrien Charvat
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Shizuka Sasazuki
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Taichi Shimazu
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Sanjeev Budhathoki
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Manami Inoue
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Motoki Iwasaki
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Norie Sawada
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Taiki Yamaji
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Shoichiro Tsugane
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
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