Zhou Hao, Vallières Martin, Bai Harrison X, Su Chang, Tang Haiyun, Oldridge Derek, Zhang Zishu, Xiao Bo, Liao Weihua, Tao Yongguang, Zhou Jianhua, Zhang Paul, Yang Li
Department of Neurology, First Xiangya Hospital, Central South University, Changsha, Hunan, China
Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
Neuro Oncol. 2017 Jun 1;19(6):862-870. doi: 10.1093/neuonc/now256.
Previous studies have shown that MR imaging features can be used to predict survival and molecular profile of glioblastoma. However, no study of a similar type has been performed on lower-grade gliomas (LGGs).
Presurgical MRIs of 165 patients with diffuse low- and intermediate-grade gliomas (histological grades II and III) were scored according to the Visually Accessible Rembrandt Images (VASARI) annotations. Radiomic models using automated texture analysis and VASARI features were built to predict isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade, and tumor progression.
Interrater analysis showed significant agreement in all imaging features scored (k = 0.703-1.000). On multivariate Cox regression analysis, no enhancement and a smooth non-enhancing margin were associated with longer progression-free survival (PFS), while a smooth non-enhancing margin was associated with longer overall survival (OS) after taking into account age, grade, tumor location, histology, extent of resection, and IDH1 1p/19q subtype. Using logistic regression and bootstrap testing evaluations, texture models were found to possess higher prediction potential for IDH1 mutation, 1p/19q codeletion status, histological grade, and progression of LGGs than VASARI features, with areas under the receiver-operating characteristic curves of 0.86 ± 0.01, 0.96 ± 0.01, 0.86 ± 0.01, and 0.80 ± 0.01, respectively.
No enhancement and a smooth non-enhancing margin on MRI were predictive of longer PFS, while a smooth non-enhancing margin was a significant predictor of longer OS in LGGs. Textural analyses of MR imaging data predicted IDH1 mutation, 1p/19q codeletion, histological grade, and tumor progression with high accuracy.
既往研究表明，磁共振成像（MR）特征可用于预测胶质母细胞瘤的生存期和分子特征。然而，尚未对低级别胶质瘤（LGG）进行类似的研究。
根据可视化可及性伦勃朗图像（VASARI）注释，对165例弥漫性低级别和中级别的胶质瘤（组织学分级为II级和III级）患者的术前MRI进行评分。利用自动纹理分析和VASARI特征构建放射组学模型，以预测异柠檬酸脱氢酶1（IDH1）突变、1p/19q共缺失状态、组织学分级和肿瘤进展。
评分者间分析显示，所有评分的影像特征均具有显著一致性（k = 0.703 - 1.000）。多因素Cox回归分析显示，无强化和光滑的无强化边缘与更长的无进展生存期（PFS）相关，而在考虑年龄、分级、肿瘤位置、组织学、切除范围和IDH1 1p/19q亚型后，光滑的无强化边缘与更长的总生存期（OS）相关。通过逻辑回归和自助检验评估发现，与VASARI特征相比，纹理模型对LGG的IDH1突变、1p/19q共缺失状态、组织学分级和进展具有更高的预测潜力，受试者操作特征曲线下面积分别为0.86±0.01、0.96±0.01、0.86±0.01和0.80±0.01。
MRI上无强化和光滑的无强化边缘可预测LGG患者更长的PFS，而光滑的无强化边缘是LGG患者更长OS的重要预测指标。MR成像数据的纹理分析可高精度预测IDH1突变、1p/19q共缺失、组织学分级和肿瘤进展。
