Microvascularity detection and quantification in glioma: a novel deep-learning-based framework
Microvascularity detection and quantification in glioma: a novel deep-learning-based framework
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神经胶质瘤的微血管检测和定量：一种基于深度学习的新型框架
DOI：
10.1038/s41374-019-0272-3
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发表时间：
2019-10-01
影响因子：
5
通讯作者：
Shi, Zhifeng
中科院分区：
文献类型：
--
作者：
Li, Xieli;Tang, Qisheng;Shi, Zhifeng
微血管分布与神经胶质瘤的分级和亚型高度相关，使其成为其最重要的组织学特征之一。微血管的准确定量分析有助于抗血管生成靶向治疗的开发。深度学习算法是迄今为止最有效的分割和检测模型，能够定位和识别从苏木精和伊红 (H&E) 染色标本获得的大图像中的复杂微血管网络。我们提出了一种基于深度学习的自动化方法来检测和量化神经胶质瘤中的微血管，并将其应用于综合临床分析。我们的研究共纳入了 350 名神经胶质瘤患者，对 H&E 染色玻片的数字化成像进行了审查，进行了分子诊断并进行了随访。根据组织学类型、分子类型和患者预后对微血管特征进行比较。结果表明，该方法能够自动有效地量化微血管特征。与其他组织学类型相比，胶质母细胞瘤的微血管密度和微血管面积显着增加（95% 密度 p < 0.001，170% p < 0.001 面积）；与其他分子类型相比，仅 TERT-mut 病例中也观察到增加（68% 密度 p < 0.001，54% p < 0.001 面积）。生存分析表明，微血管特征可用于将病例分为具有不同生存期的两组（风险比 [HR] 2.843，对数秩）
Microvascularity is highly correlated with the grading and subtyping of gliomas, making this one of its most important histological features. Accurate quantitative analysis of microvessels is helpful for the development of a targeted therapy for antiangiogenesis. The deep-learning algorithm is by far the most effective segmentation and detection model and enables location and recognition of complex microvascular networks in large images obtained from hematoxylin and eosin (H&E) stained specimens. We proposed an automated deep-learning-based method to detect and quantify the microvascularity in glioma and applied it to comprehensive clinical analyses. A total of 350 glioma patients were enrolled in our study, for which digitalized imaging of H&E stained slides were reviewed, molecular diagnosis was performed and follow-up was investigated. The microvascular features were compared according to their histologic types, molecular types, and patients' prognosis. The results show that the proposed method can quantify microvascular characteristics automatically and effectively. Significant increases of microvascular density and microvascular area were observed in glioblastomas (95% p < 0.001 in density, 170% p < 0.001 in area) in comparison with other histologic types; increases were also observed in cases with TERT-mut only (68% p < 0.001 in density, 54% p < 0.001 in area) compared with other molecular types. Survival analysis showed that microvascular features can be used to cluster cases into two groups with different survival periods (hazard ratio [HR] 2.843, log-rank