分子影像学杂志2025，Vol.48Issue(5)：614-619,6.DOI:10.12122/j.issn.1674-4500.2025.05.13
基于CT影像组学的机器学习模型可预测肺部纯磨玻璃结节的浸润性
Machine learning models based on CT radiomics can effectively predict invasiveness of pulmonary pure ground-glass nodules
摘要
Abstract
Objective To evaluate the value of a radiomics-based machine learning model in predicting the invasiveness of pulmonary pure ground-glass nodules(pGGNs).Methods A retrospective cohort study was conducted on 208 pGGNs identified in our department from August 2022 to August 2024.Based on pathological results,the nodules were classified into non-invasive and invasive groups.CT characteristics of the nodules were recorded,and radiomics features were extracted from CT images.Optimal radiomics features were selected to construct a predictive model.ROC curves were plotted and the area under the curve(AUC)was calculated.The diagnostic performance of the radiomics model was compared with that of radiologists alone and radiologists assisted by the radiomics model.Results After dimensionality reduction,six optimal features were selected to build a logistic regression model.In the training set,the model achieved an AUC,sensitivity,and specificity of 0.786,0.771 and 0.875,respectively,while in the validation set,these values were 0.776,0.735 and 0.859.The radiomics model outperformed radiologists in diagnostic accuracy and enhanced radiologists'diagnostic performance when used in combination.Conclusion The CT radiomics-based machine learning model demonstrates high predictive efficacy for determining the invasiveness of pulmonary pGGNs and provides valuable guidance for clinical decision-making.关键词
纯磨玻璃结节/影像组学/机器学习/体层摄影术/X线计算机Key words
pure ground-glass nodules/radiomics/machine learning/tomography/X-ray computed tomography引用本文复制引用
张娜,杜静,郭子泉,武志峰..基于CT影像组学的机器学习模型可预测肺部纯磨玻璃结节的浸润性[J].分子影像学杂志,2025,48(5):614-619,6.基金项目
山西省基础研究计划(202303021222327) （202303021222327）
