Puig Josep, Biarnés Carles, Daunis-I-Estadella Pepus, Blasco Gerard, Gimeno Alfredo, Essig Marco, Balaña Carme, Alberich-Bayarri Angel, Jimenez-Pastor Ana, Camacho Eduardo, Thio-Henestrosa Santiago, Capellades Jaume, Sanchez-Gonzalez Javier, Navas-Martí Marian, Domenech-Ximenos Blanca, Del Barco Sonia, Puigdemont Montserrat, Leiva-Salinas Carlos, Wintermark Max, Nael Kambiz, Jain Rajan, Pedraza Salvador
Department of Radiology, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Research Unit of Diagnostic Imaging Institute (IDI), Department of Radiology (Girona Biomedical Research Institute) IDIBGI, Hospital Universitari Dr Josep Trueta, 17007 Girona, Spain.
Cancers (Basel). 2019 Jan 14;11(1):84. doi: 10.3390/cancers11010084.
A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast⁻enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men; mean age, 58 ± 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS > 5 were classified as having highly developed macrovascular network; 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volume, increased rCBF, and poor survival; nVS correlated negatively with survival ( = -0.286; = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma.
胶质母细胞瘤中较高程度的血管生成与生存期缩短相关。临床实践中缺乏用于分析脑肿瘤血管网络的可行形态学参数。我们研究了通过三维T1加权对比增强（3D-T1CE）磁共振成像（MRI）上可见的血管样结构数量（nVS）分类的大血管网络是否能基于临床和其他影像学特征改善新诊断胶质母细胞瘤的生存预测模型。97例经组织学证实为胶质母细胞瘤的连续患者（62例男性；平均年龄58±15岁）接受了1.5T-MRI检查，包括解剖、扩散加权、动态磁敏感对比灌注以及在静脉注射0.1 mmol/kg钆布醇后的3D-T1CE序列。我们在1毫米等体素3D-T1CE图像上评估与肿瘤相关的nVS，以及对比增强病变（CEL）、非CEL和对侧正常白质感兴趣区域的相对脑血容量、相对脑血流量（rCBF）、平均通过时间和表观扩散系数。我们还评估了视觉可及的伦勃朗图像评分系统特征。我们使用ROC曲线确定nVS的临界值，并对总生存期进行单因素和多因素Cox比例风险回归分析。通过Kaplan-Meier生存分析和ROC分析评估预后因素。nVS>5的病变被分类为具有高度发达的大血管网络；58例（60.4%）肿瘤具有高度发达的大血管网络。具有高度发达大血管网络的患者年龄较大、体积较大、rCBF增加且生存期较差；nVS与生存期呈负相关（r=-0.286；P=0.008）。多因素分析显示，标准治疗、诊断时年龄和大血管网络最能预测1年生存期（AUC 0.901，灵敏度83.3%，特异度93.3%，阳性预测值96.2%，阴性预测值73.7%）。对比增强MRI大血管网络可改善新诊断胶质母细胞瘤的生存预测。
