T2*加权灌注和T1加权渗透性磁共振成像参数在确定胶质瘤IDH突变状态和分级中的预测价值

Sabahattin Yuzkan, Mehmet Karagulle, Merve Sam Ozdemir, Samet Mutlu, Burak Kocak
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引用次数: 0

摘要

目的:在胶质瘤分类中,异柠檬酸脱氢酶(IDH)突变状态至关重要,因为它对治疗方法和预后有重大影响。本研究评估了灌注和通透性磁共振成像(MRI)参数在区分2-4级胶质瘤患者的IDH基因型和肿瘤分级时的最佳临界值的性能指标:这项回顾性研究纳入了2021年11月至2023年8月期间手术诊断为2-4级胶质瘤(6例2级,7例3级,23例4级)且已知IDH基因型(23例IDH野生型,13例IDH突变型)的36例患者。所有患者术前均使用 3.0 特斯拉扫描仪进行了灌注和渗透性核磁共振成像检查。参数在彩色图谱图像上进行计算。使用类内相关系数评估观察者内部和观察者之间的一致性。经过多重检验校正后,对差异具有统计学意义的灌注参数进行了接收器操作特征(ROC)分析:结果:五项 MRI 参数(灌注的 rCBV 和 rCBF;通透性的 Ktrans、Ve 和 Vp)在 IDH 基因型组间存在显著差异(P 0.001)。在 ROC 分析中,区分 IDH 基因型的最佳参数包括 rCBV 和 Ktrans;rCBV 的临界值为 5.58,其 ROC 曲线下面积(AUC)、敏感性、特异性和准确性分别为 0.883、95.7%、76.9% 和 88.8%。对于 Ktrans(截断值为 0.0727 min-1),这些值分别为 0.893、100%、69.2% 和 88.8%。在 ROC 分析中,rCBF 和 Ve 这两个参数在区分低级别和高级别胶质瘤方面也表现良好,AUC、灵敏度和准确度分别超过 0.940、86% 和 88%:灌注和通透性磁共振成像可为区分胶质瘤的IDH基因型和分级提供有用的参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Value of T2-weighted Perfusion and T1-weighted Permeability MRI Parameters in Determining IDH Mutational Status and Grade of Gliomas.

Aim: To assess the performance metrics of perfusion and permeability magnetic resonance imaging (MRI) parameters with optimal cut-offs in differentiating isocitrate dehydrogenase (IDH) genotype and tumor grade in patients with grade 2-4 gliomas.

Material and methods: This retrospective study included 36 patients surgically diagnosed with grade 2-4 glioma (six grade 2, seven grade 3, and 23 grade 4) with known IDH genotypes (23 IDH wild-type, 13 IDH mutant) between November 2021 and August 2023. All patients underwent preoperative perfusion and permeability MRI examinations with a 3.0 Tesla scanner. Parameters were calculated on colored map images. Using the intraclass correlation coefficient, intra- and inter-observer agreement was assessed. Following multiple testing correction, the perfusion parameters with statistically significant differences were subjected to receiver operating characteristic (ROC) analysis.

Results: Five MRI parameters (rCBV and rCBF from perfusion; Ktrans, Ve, and Vp from permeability) showed a significant difference between groups in terms of IDH genotype (p < 0.001). In ROC analysis, the best parameters in differentiating IDH genotype included rCBV and Ktrans; rCBV with a cut-off of 5.58 achieved an area under the ROC curve (AUC), sensitivity, specificity, and accuracy of 0.883, 95.7%, 76.9%, and 88.8%, respectively. For Ktrans, with a cut-off of 0.0727 min-1, these values were 0.893, 100%, 69.2%, and 88.8%, respectively. In ROC analysis, these two parameters with rCBF and Ve also showed good performance in differentiating low- and high-grade gliomas with an AUC, sensitivity, and accuracy exceeding 0.940, 86%, and 88%, respectively.

Conclusion: Perfusion and permeability MRI may provide useful parameters in differentiating the IDH genotype and grade of gliomas.

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