Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in -Mutant Lower Grade Gliomas
Overview
Authors
Affiliations
Background And Purpose: ()-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 -mutant lower grade gliomas based on simple neuroimaging metrics.
Materials And Methods: One hundred two -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 -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, ( = 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 -mutant lower grade gliomas.
Byeon Y, Park Y, Lee S, Park D, Shin H, Han K NPJ Digit Med. 2025; 8(1):140.
PMID: 40044878 PMC: 11883078. DOI: 10.1038/s41746-025-01530-4.
Incorporation of Edited MRS into Clinical Practice May Improve Care of Patients with -Mutant Glioma.
Nichelli L, Cadin C, Lazzari P, Mathon B, Touat M, Sanson M AJNR Am J Neuroradiol. 2024; 46(1):113-120.
PMID: 38997123 PMC: 11735446. DOI: 10.3174/ajnr.A8413.
Lu Y, Du N, Fang X, Shu W, Liu W, Xu X Cancer Imaging. 2024; 24(1):80.
PMID: 38943156 PMC: 11212435. DOI: 10.1186/s40644-024-00726-3.
Hwa J, Wong A, Jung S, Wu C Childs Nerv Syst. 2024; 40(8):2271-2278.
PMID: 38884778 DOI: 10.1007/s00381-024-06487-5.
Ma A, Yan X, Qu Y, Wen H, Zou X, Liu X BMC Med Imaging. 2024; 24(1):85.
PMID: 38600452 PMC: 11005152. DOI: 10.1186/s12880-024-01262-z.