Analysis of the predictive value of Visually Accesable Rembrandt Images MRI feature model in high-grade glioma with IDH-1 mutations

Rui XU; Wei ZHAO; HANJIAERBIEKE·KUKUN; PAHATI·TUXUNJIANG; Chao XIE; Yifan CHANG; Yunling WANG

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Analysis of the predictive value of Visually Accesable Rembrandt Images MRI feature model in high-grade glioma with IDH-1 mutations

VernacularTitle:基于视觉能力伦勃朗图像MRI特征模型在高级别胶质瘤IDH-1突变中的预测价值分析
Author: Rui XU ¹ ; Wei ZHAO ¹ ; HANJIAERBIEKE·KUKUN ¹ ; PAHATI·TUXUNJIANG ¹ ; Chao XIE ¹ ; Yifan CHANG ¹ ; Yunling WANG ¹
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1. 新疆医科大学第一附属医院影像中心,新疆乌鲁木齐 830054
Publication Type:Journal Article
Keywords: Visually Accesable Rembrandt Images; glioma; isocitrate dehydrogenase; magnetic resonance imaging
From: Journal of Practical Radiology 2025;41(1):5-8
CountryChina
Language:Chinese
Abstract: Objective To explore the application value of constructing Visually Accesable Rembrandt Images(VASARI)MRI feature model for the prediction of isocitrate dehydrogenase-1(IDH-1)mutations in high-grade glioma(HGG).Methods A total of 242 patients with HGG were randomly divided into training set and validation set according to the ratio of 7︰3,the VASARI features of the patients were extracted to evaluate the statistical difference between IDH-1 mutant and wild-type VASARI features of the HGG.The least abso-lute shrinkage and selection operator(LASSO)regression method was used to reduce the dimension of VASARI features and the logistic regres-sion(LR)machine learning model was used to construct the prediction model for single VASARI feature and combined features.The receiver operating characteristic(ROC)curve was used to evaluate the predictive performance of the model.Results A total of 11 VASARI fea-tures were associated with IDH-1 mutations in HGG,with statistically significant differences(P<0.05).Seven closely related fea-tures were finally screened out,and the area under the curve(AUC)of the prediction model established after the combination of the seven VASARI features was higher,and the AUC of training set was 0.908,and the AUC of validation set was 0.872.Conclusion The VASARI MRI feature model can better predict IDH-1 mutations of HGG with high predictive efficacy and has greater application value.