Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign

SIMPLE SUMMARY: The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted diffuse gliomas with a high specificity and modest sensitivity. We aim to develop a multi-parametric radiomic model using MRI to predict the 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant diffuse glioma. In this retrospective study, patients with a diagnosis of IDH1 mutant gliomas with a known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. eXtremeGradient Boosting (XGboost) classifiers were used for model development. A total of 103 patients included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in determination of 1p/19q co-deletion status were 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in determination of 1p/19q non-co-deletion status and improves overall diagnostic performance of neuroradiologists when used as an assistive tool. ABSTRACT: Purpose: The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. Methods: In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. Results: A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. Conclusion: The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.