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PT  - JOURNAL ARTICLE
AU  - Batchala, P.P.
AU  - Muttikkal, T.J.E.
AU  - Donahue, J.H.
AU  - Patrie, J.T.
AU  - Schiff, D.
AU  - Fadul, C.E.
AU  - Mrachek, E.K.
AU  - Lopes, M.-B.
AU  - Jain, R.
AU  - Patel, S.H.
TI  - Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in &lt;em&gt;IDH&lt;/em&gt;-Mutant Lower Grade Gliomas
AID  - 10.3174/ajnr.A5957
DP  - 2019 Jan 31
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2019/01/31/ajnr.A5957.short
4100  - http://www.ajnr.org/content/early/2019/01/31/ajnr.A5957.full
AB  - BACKGROUND AND PURPOSE: Isocitrate dehydrogenase (IDH)-mutant lower grade gliomas are classified as oligodendrogliomas or diffuse astrocytomas based on 1p/19q-codeletion status. We aimed to test and validate neuroradiologists&#039; performances in predicting the codeletion status of IDH-mutant lower grade gliomas based on simple neuroimaging metrics.MATERIALS AND METHODS: One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor or cyst texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. To validate the classification model, 2 different independent neuroradiologists analyzed a separate cohort of 106 institutional IDH-mutant lower grade gliomas.RESULTS: Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lower grade gliomas, (n = 21); and 2) a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation of the classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers. The metrics used in the algorithm were associated with moderate-substantial interreader agreement (κ = 0.56–0.79).CONCLUSIONS: We have validated a classification algorithm based on simple, reproducible neuroimaging metrics and patient age that demonstrates a moderate prediction accuracy of 1p/19q-codeletion status among IDH-mutant lower grade gliomas.IDHisocitrate dehydrogenaseIDHmut-Codel1p/19q-codeleted IDH-mutant LGGs, oligodendrogliomasIDHmut-Noncodelnoncodeleted IDH-mutant LGGs, astrocytomasLGGlower grade gliomaMLRmultivariate logistic regressionPPVpositive predictive valueTCGAThe Cancer Genome AtlasTCIAThe Cancer Imaging ArchiveWHOWorld Health Organization