Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i. e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on ex vivo analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven de novo glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors' anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of surgically-resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.

中文翻译：

---

术前磁共振成像的多变量分析揭示了新发胶质母细胞瘤患者的转录组学分类

胶质母细胞瘤是最常见的原发性恶性脑肿瘤，具有遗传多样性，可分为四种转录组亚型，即经典型、间充质型、前神经型和神经型。目前，转录组亚型的检测是基于组织的离体分析，不能捕获空间肿瘤异质性。鉴于总结癌症分子特征的体内成像特征的累积证据，本研究仅基于常规临床获得的成像序列，寻求对胶质母细胞瘤转录组学分类可靠的非侵入性放射学标记。包括从宾夕法尼亚大学医院收集的 112 名具有多参数 MRI（T1、T1-Gd、T2、T2-FLAIR）的术前回顾性队列。在将肿瘤分割成不同的放射学子区域后，提取了不同的成像特征，并采用支持向量机对这些特征进行多变量整合，并得出转录组亚型的成像特征。提取的特征包括每个肿瘤子区域的强度分布、体积、形态、统计数据、肿瘤的解剖位置和纹理描述符。使用 5 倍交叉验证方法和接受者操作特征分析，针对手术切除的组织标本的转录组亚型评估衍生特征。所提出的模型在区分四种转录组亚型方面的准确率为 71%。区分每个亚型（经典、间充质、原神经、神经）分别等于 88.4% (71.4/92.3)、75.9% (83.9/72.8)、82.1% (73.1/84.9) 和 75.9% (79.4/74.4)。这些发现也在癌症基因组图谱胶质母细胞瘤数据集中得到了复制。经典亚型获得的成像特征主要与边缘锐度相关的特征相关，而间充质亚型在水肿中更明显地存在更高的 T2 和 T2-FLAIR 信号，以及更高的增强肿瘤和水肿体积。原神经亚型和神经亚型的特征分别是在增强肿瘤中较低的 T1-Gd 信号和在水肿中较高的 T2-FLAIR 信号。我们的结果表明，对从临床获得的 MRI 中提取的特征进行定量多变量分析可能会提供胶质母细胞瘤转录组学特征的放射学生物标志物。