| 李 俊,李为希,程颖玲,等.基于上海市社区人群低剂量CT筛查的肺癌风险预测模型研究[J].肿瘤学杂志,2024,30(8):662-670. |
| 基于上海市社区人群低剂量CT筛查的肺癌风险预测模型研究 |
| Construction of Lung Cancer Risk Prediction Model Based on Low Dose Computed Tomography Screening in Shanghai Community Population |
| 投稿时间：2024-01-22 |
| DOI：10.11735/j.issn.1671-170X.2024.08.B007 |
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| 中文关键词: 社区人群 低剂量 CT 筛查 肺肿瘤 风险预测 上海 |
| 英文关键词:community population low dose computed tomography screening lung cancer risk prediction Shanghai |
| 基金项目:上海市卫生健康委员会卫生行业临床研究专项青年项目（20204Y0109）；上海市闵行区自然科学基金（2023MHZ010） |
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| 中文摘要: |
| 摘 要：［目的］ 基于上海市社区人群低剂量CT（low dose computed tomography，LDCT）肺癌筛查项目数据，构建肺癌风险预测模型，为我国LDCT筛查高危人群的界定及后续追踪提供科学依据。［方法］ 选取2013年8月至2017年12月参与上海市闵行区肺癌LDCT筛查的合格人群24 530人，收集LDCT筛查信息、肺癌风险评估问卷信息、肺癌发病信息。采用Cox比例风险回归法共构建了两套风险预测模型：基本模型（n=24 530）纳入性别、筛查年龄、吸烟史、家族史、是否检出结节；LDCT筛查模型（n=3 649）纳入吸烟史、家族史、筛查是否阳性、结节性质、结节大小。将人群按7∶3的比例随机分为训练集和验证集，使用受试者工作特征曲线的曲线下面积（area under the curve，AUC）评价区分度，绘制校准曲线评估模型的校准度，利用十倍交叉验证方法进行预测模型的内部验证。［结果］ 24 530名研究对象的结节检出率为17.5%，LDCT筛查阳性率为12.0%，中位结节大小6.0 mm（P25，P75：4.0，10.0 mm）。在中位随访9.8年（P25，P75：8.4，11.4年）期间，共发现新发肺癌病例503例（男性342例，女性161例）。训练集中，基本模型预测1、3、5年肺癌发生风险的AUC分别为0.883、0.800和0.828，LDCT筛查模型的AUC分别为0.826、0.803和0.804，模型区分能力均较好。基本模型和LDCT筛查模型的校准曲线显示，模型拟合度均良好。十倍交叉验证结果显示，基本模型的平均AUC为0.783，标准误为0.012；LDCT筛查模型的平均AUC为0.796，标准误为0.017；模型预测效果均稳定。［结论］ 该研究建立了基于社区人群LDCT筛查的肺癌风险预测模型，其在判别能力和预测准确性方面具有良好的性能，有助于肺癌LDCT筛查高危个体的识别及筛查后健康管理。 |
| 英文摘要: |
| Abstract：［Objective］ To develop a risk predictive model for lung cancer based on a community low dose computed tomography(LDCT) screening program. ［Methods］ A total of 24 530 eligible participants of the organized lung cancer screening program in Minhang District of Shanghai during August 2013 and December 2017 were included. Data of LDCT results， questionnaire-based risk assessment， and incidence of lung cancer were collected and two risk prediction models were developed. The basic model (n=24 530) included gender， age at screening， smoking， family history of lung cancer， and nodule detection status； and the LDCT screening model (n=3 649) included smoking， family history of lung cancer， results of LDCT (positive/negative)， feature and size of detected nodules. The study population was randomly divided into training (70%) and validation (30%) sets. The area under the receiver operating characteristic curve (AUC) was used to evaluate differentiation， the calibration curves were profiled to assess the calibration of the models， and the ten-fold cross-validation method was applied for internal validation of the predictive models. ［Results］ Among 24 530 eligible participants， lung nodules were detected by LDCT in 17.5% subjects， with a positive rate of 12%. The median diameter of the nodules was 6.0 mm ［P25， P75： 4.0， 10.0 mm］. During a median of 9.8 years of follow-up ( P25， P75： 8.4， 11.4 years)， 503 subjects (342 male and 161 female) were diagnosed with lung cancer. In the training set， the AUCs of the basic model were 0.883， 0.800 and 0.828， respectively for predicting lung cancer risk within 1-， 3- and 5-year， while those for the LDCT screening model were 0.826， 0.803 and 0.804， respectively. Both models exhibited good discriminatory ability and calibration. Ten-fold cross-validation results revealed an average AUC of 0.783 with a standard error of 0.012 for the basic model， and an average AUC of 0.796 with a standard error of 0.017 for the LDCT screening model. ［Conclusion］ The risk predictive models constructed in this study perform well in predicting lung cancer risk， which have great potential for more targeted offers for LDCT screening in the populations and for further health management after screening. |
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