Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas

Abstract

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' 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 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.

Fig 1.

A 38-year-old man with a left frontal lobe diffuse astrocytoma, IDH-mutant and 1p/19q-noncodeleted, showing characteristic imaging features. A, On T2WI, the mass is homogeneously hyperintense, sharply marginated, and without significant cortical infiltration. B, FLAIR sequence shows central suppression of signal compared with the T2WI, except for a peripheral rim (ie, T2-FLAIR mismatch sign). C, T2*WI shows lack of susceptibility blooming.

Fig 2.

A 54-year-old woman with a left frontal lobe oligodendroglioma, IDH-mutant and 1p/19q-codeleted, showing characteristic imaging features. A and B, T2WI and FLAIR demonstrate a heterogeneous and poorly marginated mass with significant cortical infiltration and no T2-FLAIR mismatch sign. C, T2*WI shows regions of striking susceptibility blooming.

Fig 3.

Classification algorithm for 1p/19q-codeletion status in IDH-mutant LGGs, derived from an analysis of the TCGA/TCIA training cohort. The first step of the algorithm is an assessment of the T2-FLAIR mismatch sign; when present, it indicates, with high certainty, the IDH-mutant 1p/19q-noncodeleted subtype. When the T2-FLAIR mismatch sign is absent (or unavailable), a multivariate logistic regression model based on tumor texture, patient age, T2* blooming, tumor location, and hydrocephalus is applied. The equation for the logistic regression model is the following: ρ codeleted = exp[(Xβ) / (1+exp(Xβ)′], where Xβ = −4.8834 + 2.7842 × (texture <75% homogeneous) + 0.0587 × (patient age) + 3.1948 × (T2* susceptibility blooming present) + 1.6646 × (primary lobe = frontal) − 3.4496 × (hydrocephalus = present). A predicted probability threshold of 0.40 was established for the logistic regression model, based on the Youden J statistic. Cases were classified as either 1p/19q codeleted or non-codeleted based on whether their predicted probability was ≥ or < 0.40, respectively.