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

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 ( 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. ABBREVIATIONS: IDH isocitrate dehydrogenase; IDHmut-Codel 1p/19q-codeleted IDH-mutant LGGs, oligodendrogliomas; IDHmut-Noncodel noncodeleted IDH-mutant LGGs, astrocytomas; LGG lower grade glioma; MLR multivariate logistic regression; PPV positive predictive value; TCGA The Cancer Genome Atlas; TCIA The Cancer Imaging Archive; WHO World Health Organization The revised World Health Organization (WHO) 2016 classification of diffuse gliomas integrates isocitrate dehydrogenase (IDH) gene status and whole-arm codeletion of chromosome arms 1p and 19q with histologic findings to classify grades II and III diffuse lower grade gliomas (LGGs). More than 80% of LGGs are IDH-mutant; of those, 37%–50% carry the 1p/19q codeletion. The 1p/19q-codeleted IDH-mutant LGGs (oligodendrogliomas; IDHmut-Codel) show better overall survival compared with noncodeleted IDH-mutant LGGs (astrocytomas; IDHmut-Noncodel) and are more sensitive to adjuvant chemotherapy with procarbazine, lomustine, and vincristine. The integration of genomic data in the updated WHO classification of LGGs has accelerated efforts to noninvasively predict genetic signatures of diffuse gliomas using neuroimaging techniques. While numerous studies have identified neuroimaging features that correlate with 1p/19q-codeletion status in LGG subtypes, many were performed before the 2016 WHO update, Received November 7, 2018; accepted after revision December 12. From the Department of Radiology and Medical Imaging (P.P.B., T.J.E.M., J.H.D., S.H.P.), Department of Public Health Sciences (J.T.P.), Division of Neuro-Oncology (D.S., C.E.F.), Department of Pathology (E.K.M., M.-B.L.), Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, Virginia; and Departments of Radiology (R.J.) and Neurosurgery (R.J.), New York University School of Medicine, New York, New York. S.H. Patel was supported by a Radiological Society of North America Research Scholar Grant (RSCH1819). Preliminary data previously presented at: Annual Meeting of the American Society of Neuroradiology and the Foundation of the ASNR Symposium, June 2–7, 2018; Vancouver, British Columbia, Canada. Please address correspondence to Sohil H. Patel MD, Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA 22908; e-mail: shp4k@virginia.edu Indicates article with supplemental on-line appendix. http://dx.doi. org/10.3174/ajnr.A5957 426 Batchala Mar 2019 www.ajnr. org affecting their patient-selection process (ie, no accounting for IDH status). Moreover, these studies have applied variable imaging metrics and neuroimaging analysis, rendering it difficult to assess the relative and combined diagnostic performance of the various neuroimaging metrics reported to be associated with 1p/ 19q-codeletion status. Finally, simple metrics extrinsic to the glioma (eg, hydrocephalus, midline shift) have not been tested. The purpose of our study was to test and validate the combined accuracy of simple neuroimaging features to predict 1p/19q-codeletion status among cohorts of IDH-mutant LGGs. MATERIALS AND METHODS This was a Health Insurance Portability and Accountability Act– compliant retrospective study conducted with the University of Virginia Health System institutional review board approval. The study consisted of 2 phases. First, analysis of a training dataset yielded a multivariate classification algorithm for predicting 1p/19q-codeletion status. Second, the classification algorithm was validated using a separate dataset of cases and separate neuroradiologist readers. Training Dataset Analysis The cases composing the training dataset were accrued from The Cancer Imaging Archive (TCIA), an LGG on-line data base. TCIA data base houses MR imaging data for 199 LGGs, with molecular data (including IDH and 1p/19q statuses) available through The Cancer Genome Atlas (TCGA). The inclusion criteria were the following: 1) LGG with histopathologic assessment and grade, 2) LGG with an IDH mutation, 3) LGG with known 1p/ 19q-codeletion status, and 4) preoperative MR imaging (or MR imaging after a small-needle biopsy) containing imaging sequences relevant to the belowdescribed neuroimaging classification. IDH wild-type glioma (n 42), cases with incomplete molecular/pathologic data (n 2), and cases with insufficient MR imaging data (n 53) were excluded from the study, rendering 102 IDH-mutant LGGs included in the training dataset. Two neuroradiologists, with 5 and 13 years of experience, blinded to the 1p/ 19q-codeletion status, analyzed the MR images from the training dataset in consensus. They measured 14 neuroimaging metrics: 1) primary lobe: yes/no centered on frontal lobe; 2) texture: more or less than 75% of the tumor showing homogeneous signal intensity on T1WI/ T2WI; 3) margins: more or less than 75% of the tumor showing sharp/ circumscribed margins; 4) T2-FLAIR mismatch sign: the presence or absence of complete/near-complete hyperintense signal on T2WI and relatively hypointense signal on FLAIR except for a hyperintense peripheral rim; 5) T2* susceptibility blooming: present or absent; 6) contrast enhancement: present or absent; 7) cysts: present or absent; 8) necrosis: present or absent; 9) maximum tumor diameter (centimeter); 10) cortical infiltration: present or absent; 11) peritumoral edema: present or absent; 12) gliomatosis: yes/no involvement of 3 lobes; 13) midline shift (centimeter); and 14) hydrocephalus: present or absent. Figures 1 and 2 show the characteristic imaging appearance of IDHmut-Noncodel and IDHmut-Codel LGGs, respectively, including a description of several of the above imaging metrics. Univariate and multivariate logistic regression analysis of the MR imaging characteristics and patient age for predicting the 1p/19q-codeletion status was undertaken. On the basis of these results, a classification algorithm for 1p/19q prediction was