Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase‐Mutant Astrocytoma: A Deep Learning‐Based Radiomics Model Using MRI

Abstract

BackgroundCyclin‐dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion has been verified as an independent and critical biomarker of negative prognosis and short survival in isocitrate dehydrogenase (IDH)‐mutant astrocytoma. Therefore, noninvasive and accurate discrimination of CDKN2A/B homozygous deletion status is essential for the clinical management of IDH‐mutant astrocytoma patients.PurposeTo develop a noninvasive, robust preoperative model based on MR image features for discriminating CDKN2A/B homozygous deletion status of IDH‐mutant astrocytoma.Study TypeRetrospective.PopulationTwo hundred fifty‐one patients: 107 patients with CDKN2A/B homozygous deletion and 144 patients without CDKN2A/B homozygous deletion.Field Strength/Sequence:3.0 T/1.5 TContrast‐enhanced T1‐weighted spin‐echo inversion recovery sequence (CE‐T1WI) and T2‐weighted fluid‐attenuation spin‐echo inversion recovery sequence (T2FLAIR).AssessmentA total of 1106 radiomics and 1000 deep learning features extracted from CE‐T1WI and T2FLAIR were used to develop models to discriminate the CDKN2A/B homozygous deletion status. Radiomics models, deep learning‐based radiomics (DLR) models and the final integrated model combining radiomics features with deep learning features were developed and compared their preoperative discrimination performance.Statistical TestingPearson chi‐square test and Mann Whitney U test were used for assessing the statistical differences in patients' clinical characteristics. The Delong test compared the statistical differences of receiver operating characteristic (ROC) curves and area under the curve (AUC) of different models. The significance threshold is P < 0.05.ResultsThe final combined model (training AUC = 0.966; validation AUC = 0.935; test group: AUC = 0.943) outperformed the optimal models based on only radiomics or DLR features (training: AUC = 0.916 and 0.952; validation: AUC = 0.886 and 0.912; test group: AUC = 0.862 and 0.902).Data ConclusionWhether based on a single sequence or a combination of two sequences, radiomics and DLR models have achieved promising performance in assessing CDKN2A/B homozygous deletion status. However, the final model combining both deep learning and radiomics features from CE‐T1WI and T2FLAIR outperformed the optimal radiomics or DLR model.Evidence Level4Technical EfficacyStage 2

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

“…Overall, 41/62 of the reviewed studies (66%) focused on predicting IDH mutation and 1p/19q codeletion status only, while 33 studies (53%) analyzed other molecular subgroups. These were TERT [9,37,[40][41][42][43][44][45][46], ATRX [8,[47][48][49][50][51], H3K27 [4,[15][16][17][18], MGMT [50,[52][53][54][55], P53 [8,16,51,53], CDKN2A/B [12,30,35,56], EGFR [36], chr7/10 [57] and BRAF alterations [3,[5][6][7]. The reported AUC values range from 0.6 to 0.98 for these predictions with an average of 0.82 to 0.9.…”

Editorial for “Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase‐Mutant Astrocytoma: A Deep Learning‐Based Radiomics Model Using MRI”

“…In this issue of JMRI, Zhang et al 8 report on a radiomics model, a DLR model and a fused model for predicting CDKN2A/B status of adult astrocytomas using contrast enhanced (CE) T1W and T2 FLAIR images. The authors developed the radiomics model using 1106 features for each sequence.…”