摘要
Abstract
Objective To construct a machine learning model based on multimodal radiomic features and explore its ability to noninvasively predict the mutation status of alpha thalassemia/mental retardation syndrome X-linked(ATRX)gene in isocitrate dehydrogenase(IDH)mutant lower-grade gliomas(LrGG)preoperatively.Methods A retrospective analysis was conducted on the imaging and clinical data of 102 patients pathologically and molecularly confirmed as IDH-mutant LrGG.Of these,47 cases had A TRX mutations,and 55 cases were wild-type.Patients were randomly divided into a training set(71 cases)and a test set(31 cases)in a 7∶3 ratio.A total of 3 318 radiomic features were extracted from contrast-enhanced(CE)-T1WI,apparent diffusion coefficient(ADC)maps,and 18F-FDG PET images.The radiomic features were categorized into five datasets based on the imaging source:CE-T1WI dataset,ADC dataset.PET dataset(18F-FDG PET),MRI dataset(CE-T1WI+ADC),and combined dataset(CE-T1WI+ADC+18F-FDG PET).Four feature dimensionality reduction methods[linear discriminant analysis(LDA),principal component analysis(PCA),Wilcoxon-based correlation selection,and least absolute shrinkage and selection operator(LASSO)]and four machine learning algorithms[support vector machine(SVM),logistic regression(LR),K-nearest neighbors(KNN),random forest(RF)]were combined to construct 16 predictive models based on the combined dataset,and their performance was evaluated to determine the optimal algorithm combination.The optimal algorithm was then applied to the CE-T1WI,ADC,PET,MRI,and combined datasets to build models.Receiver operating characteristic(ROC)curves were plotted,and the area under the curve(AUC)was calculated to assess the predictive performance of each model.Results Among the 16 predictive models constructed based on the combined radiomic features,the model combining LASSO with RF had the best predictive performance,with AUCs of 0.967 and 0.950 in the training and test sets,respectively.Among the four feature reduction methods,models using LASSO showed the best overall performance;among the four machine learning algorithms,RF yielded the highest predictive performance.When applied to the CE-T1WI,ADC,PET,MRI,and combined datasets,the model demonstrated the best predictive performance in the combined dataset,with AUCs of 0.967 and 0.950 in the training test and test sets,respectively,followed by the MRI and PET datasets(AUCs of 0.931 and 0.915,respectively).Conclusion The machine learning model combining LASSO and RF algorithms based on multimodal radiomic features has high efficiency in predicting ATRX mutation status in IDH-mutant LrGG.This method is non-invasive and straight forward.关键词
胶质瘤/基因突变/影像组学/机器学习Key words
Glioma/Gene mutation/Radiomic/Machine learning分类
医药卫生
