From the Departments of Biostatistics (S.M., A.R.)
Computational Medicine & Bioinformatics (S.M., V.R., E.W., A.R.).
AJNR Am J Neuroradiol. 2022 Jan;43(1):33-39. doi: 10.3174/ajnr.A7341. Epub 2021 Nov 11.
The T2-FLAIR mismatch sign is a validated imaging sign of -mutant 1p/19q noncodeleted gliomas. It is identified by radiologists through visual inspection of preoperative MR imaging scans and has been shown to identify -mutant 1p/19q noncodeleted gliomas with a high positive predictive value. We have developed an approach to quantify the T2-FLAIR mismatch signature and use it to predict the molecular status of lower-grade gliomas.
We used multiparametric MR imaging scans and segmentation labels of 108 preoperative lower-grade glioma tumors from The Cancer Imaging Archive. Clinical information and T2-FLAIR mismatch sign labels were obtained from supplementary material of relevant publications. We adopted an objective analytic approach to estimate this sign through a geographically weighted regression and used the residuals for each case to construct a probability density function (serving as a residual signature). These functions were then analyzed using an appropriate statistical framework.
We observed statistically significant ( value = .05) differences between the averages of residual signatures for an -mutant 1p/19q noncodeleted class of tumors versus other categories. Our classifier predicts these cases with area under the curve of 0.98 and high specificity and sensitivity. It also predicts the T2-FLAIR mismatch sign within these cases with an under the curve of 0.93.
On the basis of this retrospective study, we show that geographically weighted regression-based residual signatures are highly informative of the T2-FLAIR mismatch sign and can identify -mutation and 1p/19q codeletion status with high predictive power. The utility of the proposed quantification of the T2-FLAIR mismatch sign can be potentially validated through a prospective multi-institutional study.
T2-FLAIR 不匹配征象是一种经过验证的影像征象，可用于识别 1p/19q 无突变的少突胶质细胞瘤。它是由放射科医生通过对术前磁共振成像扫描进行目视检查来识别的，并且已经证明它可以通过高阳性预测值来识别 1p/19q 无突变的少突胶质细胞瘤。我们开发了一种定量 T2-FLAIR 不匹配特征的方法，并将其用于预测低级别胶质瘤的分子状态。
我们使用来自癌症影像档案的 108 例术前低级别胶质瘤肿瘤的多参数磁共振成像扫描和分割标签。临床信息和 T2-FLAIR 不匹配征象标签是从相关出版物的补充材料中获得的。我们采用了一种客观的分析方法，通过地理加权回归来估计这个征象，并使用每个病例的残差来构建概率密度函数（作为残差特征）。然后使用适当的统计框架对这些函数进行分析。
我们观察到，在 1p/19q 无突变的肿瘤类别与其他类别的肿瘤之间，残差特征的平均值存在统计学上显著的差异（ 值=.05）。我们的分类器预测这些病例的曲线下面积为 0.98，具有高特异性和敏感性。它还可以预测这些病例中的 T2-FLAIR 不匹配征象，曲线下面积为 0.93。
基于这项回顾性研究，我们表明基于地理加权回归的残差特征高度反映了 T2-FLAIR 不匹配征象，并且可以以高预测能力识别 1p 缺失和 19q 缺失状态。所提出的 T2-FLAIR 不匹配征象定量方法的效用可以通过前瞻性多中心研究来验证。
