中国肺癌杂志2025，Vol.28Issue(4)：281-290,10.DOI:10.3779/j.issn.1009-3419.2025.102.13
基于人工智能影像学特征参数构建部分实性肺结节良恶性预测模型的应用价值
Application Value of an AI-based Imaging Feature Parameter Model for Predicting the Malignancy of Part-solid Pulmonary Nodule
摘要
Abstract
Background and objective Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths.Early-stage lung cancer is often manifested as pulmonary nodules,and accurate assess-ment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment.This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence(AI)to evaluate its effectiveness in predict-ing the malignancy of part-solid nodule(PSN).Methods This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025.According to pathological results,45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group,and 184 cases of pulmonary malignancies were categorized into the malignant group.All patients underwent preoperative chest computed tomography(CT),and AI software was used to extract imaging feature parameters.Univariate analysis was used to screen significant variables;variance inflation factor(VIF)was calculated to exclude highly collinear vari-ables,and LASSO regression was further applied to identify key features.Multivariate Logistic regression was used to determine independent risk factors.Based on the selected variables,five models were constructed:Logistic regression,random forest,XGBoost,LightGBM,and support vector machine(SVM).Receiver operating characteristic(ROC)curves were used to assess the performance of the models.Results The independent risk factors for the malignancy of PSN include roughness(ngtdm),dependence variance(gldm),and short run low gray-level emphasis(glrlm).Logistic regression achieved area under the curves(AUCs)of 0.86 and 0.89 in the training and testing sets,respectively,showing good performance.XGBoost had AUCs of 0.78 and 0.77,respectively,demonstrating relatively balanced performance,but with lower accuracy.SVM showed an AUC of 0.93 in the training set,which decreased to 0.80 in the testing set,indicating overfitting.LightGBM performed excellently in the training set with an AUC of 0.94,but its performance declined in the testing set,with an AUC of 0.88.In contrast,random for-est demonstrated stable performance in both the training and testing sets,with AUCs of 0.89 and 0.91,respectively,exhibiting high stability and excellent generalizability.Conclusion The random forest model constructed based on independent risk fac-tors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians,supporting individualized treatment decisions.关键词
肺肿瘤/部分实性肺结节/人工智能/机器学习/预测模型Key words
Lung neoplasms/Part-solid nodule/Artificial intelligence/Machine learning/Prediction model引用本文复制引用
林明治,惠一鸣,李斌,赵珮霖,郑智中,杨卓文,苏志鹏,孟于琪,宋铁牛..基于人工智能影像学特征参数构建部分实性肺结节良恶性预测模型的应用价值[J].中国肺癌杂志,2025,28(4):281-290,10.基金项目
本研究受兰州大学产学研技术开发项目(No.200458)资助 This study was supported by the grant from Technological Development Contract of Lanzhou University(No.200458)(to Bin LI). （No.200458）
