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

Autor:	Shingo Kihira, Ahrya Derakhshani, Michael Leung, Keon Mahmoudi, Adam Bauer, Haoyue Zhang, Jennifer Polson, Corey Arnold, Nadejda M. Tsankova, Adilia Hormigo, Banafsheh Salehi, Nancy Pham, Benjamin M. Ellingson, Timothy F. Cloughesy, Kambiz Nael
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Cancer Research screening and diagnosis radiogenomic Oncology and Carcinogenesis 19q co-deletion deep learning 1p/19q co-deletion diffuse glioma 1p Brain Disorders Brain Cancer Detection Rare Diseases Good Health and Well Being Oncology Clinical Research T2-FLAIR mismatch Biomedical Imaging Cancer 4.2 Evaluation of markers and technologies
Zdroj:	Cancers Volume 15 Issue 4 Pages: 1037 Cancers, vol 15, iss 4
ISSN:	2072-6694
DOI:	10.3390/cancers15041037
Popis:	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.
Databáze:	OpenAIRE
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