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目的 通过logistic回归和分类回归树（classification and regression tree, CART）构建基于临床特征与影像学特征的预测模型，用于诊断表现为肺磨玻璃结节（ground-glass nodule, GGN）样肺腺癌（lung adenocarcinoma, LAC）的浸润性。方法 引用临床试验公共管理平台中的196例GGN患者作为训练集。选取该数据集中的临床特征与影像学特征建立预测模型，并采用训练集进行模型内部验证。收集2025年1月至4月于绵阳市第三人民医院（n=48）与绵阳四〇四医院（n=36）诊断为LAC的GGN患者作为验证集，进行模型外部验证。基于训练集，采用最小绝对值收敛和选择算子（least absolute shrinkage and selection operator, LASSO）筛选模型拟合的变量。基于筛选的变量和训练集，采用logistic回归分析构建风险预测模型。采用受试者工作特征曲线和Hosmer-Lemeshow检验验证模型区分度和一致性。采用CART计算模型风险评分截断值。结果 多因素logistic回归分析显示，平均CT值、实性成分直径和胸膜牵拉征均是浸润性腺癌（invasive adenocarcinoma, IA）的独立危险因素（均P<0.05），并据此建立IA预测模型如下：P0=ex/（1+ex），其中P0为预测概率，风险评分x=0.002 9×平均CT值（HU）+0.236 2×实性成分直径（mm）+0.998 8×胸膜牵拉征（是=1，否=0）。内部验证显示，该模型敏感度为80.2%，特异度为86.0%，约登指数（Youden's index, YI）为0.662，曲线下面积（area under the curve, AUC）为0.893（95%CI:0.849～0.937）。外部验证显示，该模型敏感度为92.7%，特异度为69.8%,YI为0.625,AUC为0.875（95%CI:0.802～0.948）。该模型具有良好的内部（P=0.645）和外部（P=0.436）稳定性。风险评分截断值为-0.09。其预测浸润性GGN的准确度为81.6%，阳性预测值为78.9%，阴性预测值为84.8%，敏感度为85.4%，特异度为84.8%。结论 本研究基于风险评分构建的预测模型在诊断浸润性GGN上具有较高的应用价值，可有效评估IA的风险。
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 selected 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 operating 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 specificity 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 IA.
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基本信息:
DOI：10.3785/syzlzz.2026.048
引用信息:
[1]陈娟娟,杨自力,党好,等.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],2026,41(4):344-351.DOI:10.3785/syzlzz.2026.048.
基金信息:
成都市卫生健康委员会医学科研项目(2022070)