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.).
AJNR Am J Neuroradiol. 2019 Mar;40(3):426-432. doi: 10.3174/ajnr.A5957. Epub 2019 Jan 31.
()-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.
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.
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).
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.
根据 1p/19q 缺失状态,()-突变型低级别胶质瘤被分类为少突胶质细胞瘤或弥漫性星形细胞瘤。我们旨在测试和验证神经放射科医生基于简单神经影像学指标预测()-突变型低级别胶质瘤缺失状态的能力。
从癌症基因组图谱中,共有 102 例术前磁共振成像(MRI)且已知 1p/19q 状态的()-突变型低级别胶质瘤组成训练数据集。两名神经放射科医生在共识的基础上对训练数据集进行了各种影像学特征分析:肿瘤纹理、边缘、皮质浸润、T2-FLAIR 不匹配、肿瘤囊肿、T2*敏感性、脑积水、中线移位、最大尺寸、原发叶、坏死、增强、水肿和神经胶质瘤病。对训练数据的统计分析产生了基于磁共振成像特征和患者年龄的亚组的缺失预测的多变量分类模型。为了验证分类模型,2 名不同的独立神经放射科医生分析了 106 例机构性()-突变型低级别胶质瘤的独立队列。
训练数据集分析产生了一个两步分类算法,其缺失预测准确率为 86.3%,基于以下特征:1)T2-FLAIR 不匹配征象的存在,该征象对非缺失型低级别胶质瘤具有 100%的预测性(=21);2)基于纹理、患者年龄、T2*敏感性、原发叶和脑积水的逻辑回归模型。分类算法的独立验证在 2 名独立读者中得出了 81.1%和 79.2%的缺失预测准确率。算法中使用的指标与中度-高度的观察者间一致性相关(κ=0.56-0.79)。
我们验证了一种基于简单、可重复的神经影像学指标和患者年龄的分类算法,该算法在()-突变型低级别胶质瘤中显示出 1p/19q 缺失状态的中等预测准确性。