川北医学院学报2026，Vol.41Issue(4)：422-427,6.DOI:10.3969/j.issn.1005-3697.2026.04.005
XGBoost-SHAP肺结节早期识别可解释性框架构建
XGBoost-SHAP-based interpretable framework for the early identification of pulmonary nodules
摘要
Abstract
Objective:To achieve early identification of pulmonary nodules and visual interpretation of key variables through interpretable machine learning,and to facilitate precise prevention,control,early diagnosis and treatment of lung cancer.Methods:This study enrolled individuals at high risk of lung cancer and completed clinical screening.Their high-risk assessment data and imaging results were extracted.Participants were divided into high-risk and low-risk groups for pulmonary nodules based on China's Lung Cancer Screening Standard(T/CPMA 013-2020).Variables with differences identified by univariate analysis were used as predictors,with pulmonary nodule grouping as the dependent variable,to construct an interpretable XGBoost-SHAP identification framework for early nodule detection and visual result interpretation.Results:A total of 644 high-risk individuals were included,with 199(30.9%)in the high-risk pulmonary nodule group.The XGBoost model achieved an accuracy of 0.914 6,sensitivity of 0.758 7,specificity of 0.984 3,F1-score of 0.845 8,and AUC of 0.974 1 for nodule grouping.SHAP analysis revealed that higher SHAP values-and thus increased risk of nodule enlargement-were associated with greater smoking intensity,exposure to secondhand smoke from colleagues/family,infrequent kitchen ventilation during cooking,excessive intake of processed foods,occupational exposure to asbestos/radon,insufficient intake of protein,fruits and vegetables,and manual labor occupation.Conclusion:The constructed interpretable framework performs well in early pulmonary nodule identification.Changes in nodule size are associated not only with traditional risk factors(e.g.,smoking habits,secondhand smoke exposure,cooking fume exposure,occupational asbestos/radon exposure)but also with the participants'dietary habits.关键词
肺结节/早期识别/XGBoost/SHAP/可解释性框架Key words
Pulmonary nodules/Early identification/XGBoost/SHAP/Interpretable framework分类
医药卫生引用本文复制引用
易付良,邹雪娜,李刚,刘昕,向茹梅,骆长玲,邓丽春,余秀莲,周厚容,高扬..XGBoost-SHAP肺结节早期识别可解释性框架构建[J].川北医学院学报,2026,41(4):422-427,6.基金项目
成都医学院教育发展基金会科研专项项目(25LHZG-12) （25LHZG-12）
四川省自贡市重点科技计划项目(2024-YGY-01-04) （2024-YGY-01-04）
