基于T1WI增强不同机器学习模型鉴别胶质母细胞瘤与原发性中枢神经系统淋巴瘤OACSTPCD

Objective:To construct models for predicting glioblastoma(GBM)and primary central nervous system lymphoma(PCNSL)using six different machine learning classification algorithms based on T1WI-enhanced images,and to compare the diagnostic efficacy of different machine learning models.Methods:A retrospective analysis was conducted on the clinical and imaging data of 57 patients with pathologically confirmed GBM and 49 patients with PCNSL at Xiangya Hospital of Central South University.The ITK-SNAP software was used to manually outline the tumor region of interest(ROI)layer by layer on preoperative T1WI-enhanced images.ROI imaging omics features were extracted based on the Radcloud platform,and the vari-ance threshold method(threshold>0.9),univariate feature selection method(P<0.01),and least absolute shrinkage and selection operator(LASSO)were used for feature dimensionality reduction,and the screened features were used to construct an ima-geomics prediction model using six classifiers including support vector machine,extreme gradient boosting,Logistic regression(LR),linear discriminant analysis(LDA),random forest,and K-nearest neighbor.The 5-fold cross-validation method was used for validation,and the diagnostic performance of six predictive models was evaluated using the area under the curve(AUC),and the DeLong test was used for AUC comparisons between models.Results:A total of 1688 imaging omics features were extracted,and the significant features(25,10,31,17,and 14 features per group in 5-fold cross-validation,respectively)were retained after feature dimensionality reduction and screening to construct the prediction models,and among the 6 models,the LDA and LR models had the best diagnostic efficacy,and the average AUC of the LDA and LR models in the 5-fold cross-validation set was 0.965 and 0.958,respectively,with accuracy of 87.8%and 89.6%,sensitivity of 86.0%and 86.0%,and specificity of 89.4%and 93.0%.There was no statistically significant differences in AUC among the six models(P>0.05).Conclusion:Machine learning models based on T1WI-enhanced image omics features can be used to predict GBM and PC-NSL with high accuracy,among which LDA and LR models have the best diagnostic efficacy.