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大的规模用户链表生话现象方案对应的肺肿瘤概率預测模板的实现

陈睿琳,  王静茹,  王硕,  koko体育app: 唐思琦,  koko体育app: 索晨

陈睿琳, 王静茹, 王硕, 等. 大规模人群队列生活行为方式相关的肺癌风险预测模型的构建[J]. koko体育app 学报（医学版）, 2023, 54(5): 892-898. doi: 10.12182/20230960209
引用本文: 陈睿琳, 王静茹, 王硕, 等. 大规模人群队列生活行为方式相关的肺癌风险预测模型的构建[J]. koko体育app 学报（医学版）, 2023, 54(5): 892-898. doi:
CHEN Ruilin, WANG Jingru, WANG Shuo, et al. Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCES), 2023, 54(5): 892-898. doi: 10.12182/20230960209
Citation: CHEN Ruilin, WANG Jingru, WANG Shuo, et al. Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCES), 2023, 54(5): 892-898. doi:

大规模人群队列生活行为方式相关的肺癌风险预测模型的构建

doi: 
  • 1. 清华综合大学公用设施清洁系 时兴病学教研组室 （沪 200032）
  • 2. 成都市重特大接触传得病和生态学应急调查院 （成都 200032）
  • 3. 复旦上大学上大学泰州安全健康科学性研究探讨院 （泰州 225316）
基金项目: 国家重点研发计划项目（No. 2022YFC3400700、No. 2019YFC1315804），上海市公共卫生体系建设三年行动计划优秀人才项目（No. GWV-10.2-YQ32），上海市市级科技重大专项（No. ZD2021CY001）和上海科技委员会创新基金（No. 20ZR1405600）资助
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    通讯作者:

    E-mail：koko体育app:suochen@fudan.edu.cn

Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort

  • 1. Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
  • 2. Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China
  • 3. Fudan University Taizhou Institute of Health Sciences, Taizhou 225316, China
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  • 摘要:   目的  发现影响肺癌发病的生活行为相关危险因素，并构建肺癌风险预测模型，识别人群中的高风险个体，帮助肺癌早期筛查。  方法  本研究数据来源于英国生物样本库（UK Biobank）2006年3月–2010年10月收集的502389名参与者。参考国内外肺癌筛查指南和高质量肺癌危险因素研究文献，确定本研究高危人群识别标准。采用单因素Cox回归分析及逐步回归筛选出肺癌的危险因素，通过Cox比例风险回归构建多因素肺癌风险预测模型，根据比较赤池信息准则以及Schoenfeld残差检验结果，最终选择等比例假设的最优拟合模型。多因素Cox比例风险回归考虑生存时间，将人群按7∶3的比例随机分为训练集和验证集，使用训练集建立模型，并用验证集对模型性能进行内部验证。受试者工作特征曲线（ROC）曲线的曲线下面积（AUC）被用于评估模型的效能。将人群按照发病概率的0%～<25%、25%～<75%、75%～100%分为低风险、中风险及高风险人群，分别计算其中的发病人数占比。  结果  本研究最终纳入453558人，在累计随访5505402人年期间，共诊断出2330例肺癌。Cox比例风险回归分析筛选出10个自变量建立模型：年龄、体质量指数（body mass index, BMI）、学历、收入、体力活动情况、吸烟状态、饮酒频率、新鲜水果摄入量、癌症家族史、烟草暴露。该模型通过内部验证结果显示8个自变量（除BMI和新鲜水果摄入量外）均是肺癌的影响因素（ P<0.05）。该模型训练集预测肺癌发生的一年、五年、十年AUC分别为0.825、0.785、0.777；验证集预测肺癌发生的一年、五年、十年AUC分别为0.857、0.782、0.765。筛查高风险人群可发现68.38%的未来肺癌发病个体。  结论  本研究建立了大规模人群生活行为方式相关的肺癌风险预测模型，其在判别能力方面表现出良好的性能，为制定肺癌标准化筛查策略提供了工具。
    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 502389 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 453558 individuals, and out of the cumulative follow-up of 5505402 person-years, a total of 2330 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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    图  1  危险因素筛选流程

    Figure  1.  Risk factor screening process

    图  2  训练集（左）和验证集（右）ROC曲线分析结果

    Figure  2.  ROC curve aꦺnalysis results of the training set (left) and the t𝐆est set (right)

    表  1  统计学特征及Cox回归分析

    Table  1.   Sꦆtatistical characteristics and Cox regression analysis

    VariableLung
    cancer/case,
    n=2330    		No lung
    cancer/case,
    n=451228    		Incidence/
    ‰    		Univariable CoxMultivariate Cox
    HR (95% CI)PHR (95% CI)P
    Age/yr.
    　40-49 91 111462 0.82 Reference Reference
    　50-64 1347 259088 5.17 6.55 (5.06-8.46) <0.001 5.43 (3.39-8.69) <0.001
    　≥65 892 80678 10.94 14.46 (11.14-18.76) <0.001 11.36 (6.95-18.56) <0.001
    Sex
    　Female 1045 240686 4.32 Reference
    　Male 1285 210542 6.07 1.43 (1.3-1.58) <0.001
    Body mass index/(kg/m2)
    　<18.5 52 4537 11.33 Reference Reference
    　18.5-24.9 724 145402 4.95 0.47 (0.33-0.66) <0.001 3.46 (0.48-24.77) 0.22
    　25.0-29.9 971 191225 5.05 0.48 (0.34-0.67) <0.001 3.11 (0.44-22.23) 0.26
    　>29.9 583 110064 5.27 0.48 (0.34-0.68) <0.001 2.61 (0.36-18.73) 0.34
    Qualifications
    　College or university degree 362 147542 2.45 Reference Reference
    　A levels/AS levels or equivalent 181 50323 3.58 1.62 (1.31-1.99) <0.001 1.66 (1.23-2.23) <0.001
    　O levels/GCSEs or equivalent 424 94697 4.46 1.87 (1.58-2.21) <0.001 1.57 (1.22-2.03) <0.001
    　CSEs or equivalent 77 24866 3.09 1.28 (0.95-1.72) 0.105 1.11 (0.65-1.88) 0.71
    　NVQ, or HND, or HNC, or the equivalent 207 29622 6.94 2.95 (2.41-3.62) <0.001 1.71 (1.22-2.38) <0.001
    　Other professional qualifications 117 22960 5.07 2.06 (2.41-2.65) <0.001 1.55 (1.06-2.25) 0.02
    　Missing data 962 81218 11.71
    Income/(£/year)
    　<18000 826 85352 9.58 Reference Reference
    　18000-30999 564 96737 5.80 0.62 (0.54-0.70) <0.001 1.02 (0.79-1.32) 0.90
    　31000-51999 299 101496 2.94 0.32 (0.27-0.37) <0.001 0.87 (0.66-1.16) 0.35
    　52000-100000 146 80277 1.82 0.19 (0.16-0.24) <0.001 0.63 (0.44-0.89) <0.001
    　>100000 39 21455 1.81 0.17 (0.11-0.26) <0.001 0.83 (0.49-1.41) 0.50
    　Missing data 456 65911 6.87
    Ethnicity
    　White 2250 423934 5.28 Reference
    　Not white 70 25677 2.72 0.46 (0.34-0.62) <0.001
    　Missing data 10 1617 6.15
    International Physical Activity Questionnaires activity group
    　Low 417 68539 6.05 Reference Reference
    　Moderate 687 148113 4.62 0.78 (0.67-0.90) 0.001 0.75 (0.58-0.95) 0.02
    　High 639 147235 4.32 0.73 (0.63-0.85) <0.001 0.61 (0.47-0.79) <0.001
    　Missing data 587 87341 6.68
    Smoking status
    　Never 308 249020 1.24 Reference Reference
    　Previous 1029 153457 6.66 5.70 (4.89-6.64) <0.001 3.74 (2.98-4.68) <0.001
    　Current 975 47071 20.29 16.93 (14.51-19.76) <0.001 3.23 (1.98-5.29) <0.001
    　Missing data 18 1680 10.60
    Cooked vegetables intake/(tablespoon/d)
    　<3 1177 230290 5.08 Reference
    　3 604 120535 4.99 0.98 (0.87-1.10) 0.691
    　>3 506 94436 5.33 1.08 (0.96-1.23) 0.195
    　Missing data 43 5967 7.15
    Salad/Raw vegetables intake/(tablespoon/d)
    　<3 1657 305853 5.39 Reference
    　3 270 62888 4.27 0.80 (0.69-0.93) 0.004
    　>3 345 76357 4.50 0.83 (0.72-0.95) 0.007
    　Missing data 58 6130 9.37
    Fresh fruit intake/(pieces/d)
    　<3 1651 286932 5.72 Reference Reference
    　3 352 89765 3.91 0.66 (0.57-0.76) <0.001 0.80 (0.61-1.03) 0.08
    　>3 298 72558 4.09 0.75 (0.65-0.87) <0.001 1.05 (0.82-1.36) 0.69
    　Missing data 29 1973 14.49
    Meat intake/ (tablespoon/d)
    　<1 725 178138 4.05 Reference
    　1 677 131150 5.14 1.21 (1.06-1.37) 0.003
    　>1 923 140841 6.51 1.59 (1.41-1.78) <0.001
    　Missing data 5 1099 4.53
    Cheese intake/(tablespoon/d)
    　<1 509 88452 5.72 Reference
    　1 543 94305 5.72 1.04 (0.90-1.20) 0.635
    　>1 1198 257268 4.64 0.85 (0.75-0.96) 0.011
    　Missing data 80 11203 7.09
    Alcohol drinker status
    　Never 78 20245 3.84 Reference
    　Previous 186 15949 11.53 3.26 (2.34-4.54) <0.001
    　Current 2063 414475 4.95 1.45 (1.09-1.93) 0.010
    　Missing data 3 559 5.34
    Alcohol drinking status/(day/week)
    　>4 577 91413 6.27 Reference Reference
    　1-4 974 221206 4.38 0.69 (0.61-0.78) <0.001 0.79 (0.63-1.00) 0.04
    　<1 776 138186 5.58 0.88 (0.77-1.00) 0.052 1.06 (0.81-1.38) 0.67
    　Missing data 3 423 7.04
    Worries/anxious feelings
    　No 1023 191389 5.32 Reference
    　Yes 1229 247351 4.94 0.89 (0.81-0.98) 0.021
    　Missing data 78 12488 6.21
    Apolipoprotein A
    　Low 9 663 13.39 Reference
    　Moderate 1974 375222 5.23 0.38 (0.17-0.85) 0.019
    　High 42 12017 3.48 0.26 (0.11-0.62) 0.003
    　Missing data 305 63326 4.79
    Apolipoprotein B
    　Low 2 255 7.78 Reference
    　Moderate 1697 325484 5.19 0.90 (0.13-6.41) 0.918
    　High 472 96954 4.84 0.83 (0.12-5.94) 0.857
    　Missing data 159 559 221.45
    High-density lipoprotein
    　Low 500 70339 7.06 Reference
    　Moderate 971 184553 5.23 0.73 (0.65-0.84) <0.001
    　High 562 135067 4.14 0.60 (0.52-0.69) <0.001
    　Missing data 297 61269 4.82
    Low-density lipoprotein
    　Low 155 15239 10.07 Reference
    　Moderate 710 117309 6.02 0.65 (0.52-0.80) <0.001
    　High 1313 291491 4.48 0.48 (0.39-0.59) <0.001
    　Missing data 152 27189 5.56
    Total cholesterol
    　Low 13 1181 10.89 Reference
    　Moderate 916 145186 6.27 0.56 (0.29-1.09) 0.088
    　High 1253 278430 4.48 0.40 (0.21-0.78) 0.007
    　Missing data 148 26431 5.57
    Triacylglycerol
    　Low 21 6735 3.11 Reference
    　Moderate 1123 248222 4.50 1.37 (0.83-2.24) 0.215
    　High 1036 169510 6.07 1.75 (1.07-2.88) 0.026
    　Missing data 150 26761 5.57
    Family history of cancer
    　No 1862 392298 4.72 Reference Reference
    　Yes 452 56907 7.88 1.75 (1.55-1.97) <0.001 1.65 (1.30-2.09) <0.001
    　Missing data 16 2023 7.85
    Tobacco exposure
    　No 1033 322997 3.19 Reference Reference
    　Yes 396 87133 4.52 1.47 (1.28-1.68) <0.001 1.33 (1.07-1.65) 0.01
    　Missing data 901 41098 21.45
    　A levels: Advanced Level qualifications; AS levels: Advanced subsidiary levels; O levels: General Certificate of Education Ordinary Level; GCSEs: General Certificate of Secondary Education; CSEs: Certificate of Secondary Education; NVQ: National Vocational Qualification; HND: higher National Diploma; HNC: Higher National certificate.
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    表  2  人群风险评估结果

    Table  2.   Results of population risk assessment

    Risk situationNumber of
    cases    		Proportion of
    cases
    High-risk population 291 68.38%
    Moderate-risk population 123 28.58%
    Low-risk population 13 3.04%
    Total 427 100%
    下载: 导出CSV
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  • 收稿日期:  2024-06-30
  • 修回日期:  2024-08-30
  • 刊出日期:  2024-10-13

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