Quantifying T2-FLAIR Mismatch Using Geographically Weighted Regression and Predicting Molecular Status in Lower-Grade Gliomas

BACKGROUND AND PURPOSE:

The T2-FLAIR mismatch sign is a validated imaging sign of isocitrate dehydrogenase–mutant 1p/19q noncodeleted gliomas. It is identified by radiologists through visual inspection of preoperative MR imaging scans and has been shown to identify isocitrate dehydrogenase–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.

MATERIALS AND METHODS:

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.

RESULTS:

We observed statistically significant (P value = .05) differences between the averages of residual signatures for an isocitrate dehydrogenase–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.

CONCLUSIONS:

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 isocitrate dehydrogenase–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.