实用肿瘤杂志2026，Vol.41Issue(4)：344-351,8.DOI:10.3785/syzlzz.2026.048
基于logistic回归与分类回归树建立的预测模型对磨玻璃结节样浸润性肺腺癌的诊断价值
Value of a prediction model based on logistic regression and classification and regression tree in diagnosing invasive lung adenocarcinoma manifesting as ground-glass nodules
摘要
Abstract
Objective To establish a prediction model using clinical and imaging features based on logistic regression and classification and regression tree(CART)for diagnosing invasive lung adenocarcinoma(LAC)manifesting as ground-glass nodules(GGNs).Methods Citing the data of 196 GGN cases from the Research Manager as the training set.The clinical and imaging features from the data were se-lected to establish a prediction model.The training set was used to conduct internal validation.GGN patients diagnosed as LAC at the Third Hospital of Mianyang(n=48)and Mianyang 404 Hospital(n=36)from January to April 2025 were enrolled as the validation set to conduct external validation.Least absolute shrinkage and selection operator(LASSO)was used for variable screening using the training set.Based on the screened variables and the training set,multivariate logistic regression was used to construct a risk prediction model.Receiver oper-ating characteristic curve and Hosmer-Lemeshow test were used to validate the discrimination and calibration.CART analysis was used to calculate the cut-off value of the risk score.Results Multivariate logistic regression analysis showed the mean CT value,the diameter of solid component,and pleural indentation sign to be independent risk factors for invasive adenocarcinoma(all P＜0.05),and thus a prediction model for invasive adenocarcinoma was constructed as P0=ex/(1+ex),in which P0 represented predicted probablity,and the risk score x=0.002 9 × mean CT value(HU)+0.236 2 × diameter of solid component(mm)+0.998 8 × pleural indentation sign(yes=1,no=2).Internal verification showed that the model had a sensitivity of 80.2％and a specificity of 86.0％with Youden's index(YI)at 0.662 and area under the curve(AUC)of 0.893(95％CI:0.849-0.937).External verification showed that the model had a sensitivity of 92.7％and a spec-ificity of 69.8％with YI at 0.625 and AUC of 0.875(95％CI:0.802-0.948).Calibration curves indicated good internal and external stability(P=0.645,P=0.436).The cut-off value of the risk score was-0.09,and it had an accuracy of 81.6％,a positive predictive value of 78.9％,a negative predictive value of 84.8％,a sensitivity of 85.4％,and a specificity of 84.8％for predicting invasive GGNs.Conclusions This model based on risk score has a high application value for diagnosing invasive GGNs,and can effectively assess the risk of I A.关键词
浸润性肺腺癌/磨玻璃结节/分类回归树/预测模型/诊断Key words
invasive lung adenocarcinoma/ground-glass nodules/classification and regression tree/prediction model/diagnosis引用本文复制引用
陈娟娟,杨自力,党好,于民浩..基于logistic回归与分类回归树建立的预测模型对磨玻璃结节样浸润性肺腺癌的诊断价值[J].实用肿瘤杂志,2026,41(4):344-351,8.基金项目
成都市卫生健康委员会医学科研项目(2022070) （2022070）
