CT特征联合临床因素的机器学习模型预测部分实性结节肺腺癌气腔播散的价值

Value of the Machine Learning Model That Combines CT Features with Clinical Factors in Predicting Spread through Air Space in Partially Solid Nodules of Lung Adenocarcinoma

摘要: 目的：探究CT特征联合临床因素的机器学习模型预测部分实性结节肺腺癌气腔播散(STAS)的价值。方法：回顾性收集152例经病理证实的部分实性结节肺腺癌患者，将其随机分为训练组(106例)及验证组(46例)。采用单因素及多因素logistic回归分析临床资料、CT特征，确定STAS状态的独立预测因素。采用逻辑回归、多层感知器、随机森林(RF)及朴素贝叶斯算法建立机器学习模型，受试者工作特征曲线下面积(AUC)评估模型的预测效能。结果：单因素及多因素logistic回归分析显示，癌胚抗原、肿瘤最大径、cT分期、毛刺征是STAS状态的独立预测因素。以上述变量构建的机器学习模型中，RF模型展现了良好的预测效能，在训练组及验证组AUC分别为0.920和0.859。结论：CT特征联合临床因素的机器学习模型对部分实性结节肺腺癌STAS具有较好的预测价值。

Abstract: Objective: To investigate the value of the machine learning model incorporating CT features and clinical factors in predicting spread through air spaces (STAS) in lung adenocarcinoma presenting as part-solid nodules. Methods: A total of 152 patients with pathologically confirmed lung adenocarcinoma manifesting as part-solid nodules were retrospectively enrolled and randomly divided into a training cohort (n = 106) and a validation cohort (n = 46). Univariate and multivariate logistic regression analyses were performed on clinical data and CT features to identify independent predictors of STAS status. Machine learning models were constructed using logistic regression, multilayer perceptron, random forest (RF), and naive bayes algorithms. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Univariate and multivariate logistic regression analysis identified carcinoembryonic antigen level, maximum tumor diameter, cT stage, and spiculation as independent predictors of STAS status. Among the machine learning models built with these variables, RF model demonstrated favorable predictive performance, with AUCs of 0.920 in the training cohort and 0.859 in the validation cohort. Conclusion: The machine learning model combining CT features and clinical factors show good predictive value for STAS in lung adenocarcinoma presenting as part-solid nodules.