Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Neurosurgery Department, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China 250021.
Biomed Res Int. 2020 Oct 24;2020:4630218. doi: 10.1155/2020/4630218. eCollection 2020.
BACKGROUND: The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. PURPOSE: To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. MATERIALS AND METHODS: A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. RESULTS: The radiomic model showed good discrimination in both the primary dataset (AUC = 0.87, 95% CI: 0.754 to 0.855, ACC = 0.798, sensitivity = 85.5%, specificity = 75.4%, positive predictive value = 0.734, and negative predictive value = 0.867) and the validation dataset (AUC = 0.86, 95% CI: 0.690 to 0.913, ACC = 0.789, sensitivity = 91.3%, specificity = 69.0%, positive predictive value = 0.700, and negative predictive value = 0.909). CONCLUSION: The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.
背景：IDH1 突变型胶质瘤的预后明显优于野生型胶质瘤，术前识别胶质瘤中的 IDH 突变对于制定手术方案和预后评估至关重要。
目的：探讨基于术前增强磁共振成像的放射组学模型在高级别胶质瘤 IDH1 基因型评估中的价值。
材料与方法：回顾性分析了 2012 年 12 月至 2019 年 1 月在我院接受手术病理证实的 182 例高级别胶质瘤患者的完整术前脑增强磁共振成像资料，其中 IDH1 突变 79 例（WHO 分级 III 级 45 例，WHO 分级 IV 级 34 例），IDH1 野生型 103 例（WHO 分级 III 级 33 例，WHO 分级 IV 级 70 例）。采用分层随机抽样法将患者分为原始数据集和验证数据集，比例为 7∶3；使用 AK（Analysis Kit，GE Healthcare）软件提取放射组学特征，采用 Kruskal-Wallis 和 Spearman 分析进行初步降维。最后使用 Lasso 获得特征的最优子集，构建放射组学模型，并通过交叉验证进行测试。采用 ROC（Receiver Operating Characteristic Curve）分析评估模型的性能。
结果：该放射组学模型在原始数据集（AUC = 0.87，95%CI：0.754～0.855，ACC = 0.798，敏感性为 85.5%，特异性为 75.4%，阳性预测值为 0.734，阴性预测值为 0.867）和验证数据集（AUC = 0.86，95%CI：0.690～0.913，ACC = 0.789，敏感性为 91.3%，特异性为 69.0%，阳性预测值为 0.700，阴性预测值为 0.909）中均具有良好的判别能力。
结论：基于术前增强磁共振成像的放射组学模型可以有效地预测高级别胶质瘤的 IDH1 基因型。
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