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

被引:75
|
作者
Batchala, P. P. [1 ,2 ]
Muttikkal, T. J. E. [1 ,2 ]
Donahue, J. H. [1 ,2 ]
Patrie, J. T. [3 ,4 ]
Schiff, D. [5 ,6 ]
Fadul, C. E. [5 ,6 ]
Mrachek, E. K. [7 ,8 ]
Lopes, M-B. [7 ,8 ]
Jain, R. [9 ,10 ]
Patel, S. H. [1 ,2 ]
机构
[1] Univ Virginia Hlth Syst, Dept Radiol & Med Imaging, Div Neuropathol, Charlottesville, VA USA
[2] Univ Virginia Hlth Syst, Dept Radiol & Med Imaging, Div Mol Diagnost, Charlottesville, VA USA
[3] Univ Virginia Hlth Syst, Dept Publ Hlth Sci, Div Neuropathol, Charlottesville, VA USA
[4] Univ Virginia Hlth Syst, Dept Publ Hlth Sci, Div Mol Diagnost, Charlottesville, VA USA
[5] Univ Virginia Hlth Syst, Div Neurooncol, Div Neuropathol, Charlottesville, VA USA
[6] Univ Virginia Hlth Syst, Div Neurooncol, Div Mol Diagnost, Charlottesville, VA USA
[7] Univ Virginia Hlth Syst, Dept Pathol, Div Neuropathol, Charlottesville, VA USA
[8] Univ Virginia Hlth Syst, Dept Pathol, Div Mol Diagnost, Charlottesville, VA USA
[9] NYU, Dept Radiol, Sch Med, 560 1St Ave, New York, NY 10016 USA
[10] NYU, Dept Neurosurg, Sch Med, 550 1St Ave, New York, NY 10016 USA
关键词
OLIGODENDROGLIAL TUMORS; MOLECULAR DIAGNOSTICS; 1P/19Q CODELETION; MR; PERFUSION; DELETION; IDH1-MUTATION; SPECTROSCOPY; CHEMOTHERAPY; SIGNATURES;
D O I
10.3174/ajnr.A5957
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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 (kappa = 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.
引用
收藏
页码:426 / 432
页数:7
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