数字病理学在改善现有诊断方法的工具方面取得了长足的进展。然而，神经病理学等几个病理学领域仍然面临机器学习工具和神经网络分析的覆盖率较低的问题，这可能是由于相应肿瘤如胶质母细胞瘤的细胞和分子结构的复杂性所致。在本研究框架下，我们使用先进的专有工具获取组织学切片图像并进行深度形态学分析，研究了198位胶质母细胞瘤患者的样本，选择了形态学细胞簇。此外，对每个簇中的细胞进行了分离，并研究了它们的增殖、迁移、浸润活性、存活能力、有氧糖酵解活性以及化疗和放疗抗性。我们确定了四个形态学簇，包括小细胞簇、副核内簇、低染色簇和大核簇，它们在形态学参数和功能参数上显著不同。低染色簇细胞显示出最高的增殖活性；大核簇是最活跃的葡萄糖消耗者；副核内簇具有最突出的迁移和浸润活性以及耐受缺氧能力；小细胞簇主要表现为各参数的平均值。此外，额外的分析揭示了一个单独的干细胞亚簇，其分子特性与胶质母细胞瘤干细胞相对应，并存在于这四个簇中。还发现了胶质母细胞瘤的几个关键分子参数，如EGFR、PDGFRA和NF1基因的突变修饰，以及分子GBM亚型与已确定的细胞簇显著相关。因此，这些结果代表了数字病理学在实际领域和胶质瘤癌发病机制的基本问题上的一项值得期待的创新。 版权所有 © 2023。由Elsevier Ltd.出版。

Digital pathology has come a long way in terms of creating tools to improve existing diagnostic approaches. However, several pathology fields, such as neuropathology, are still characterized by low coverage from machine learning tools and neural network analysis, which may be due to the complexity of the internal cellular and molecular structure of the corresponding neoplasms, including glioblastomas.In the framework of this study, using advanced proprietary tools for obtaining images of histological slides and their deep morphometric analysis, we studied samples of 198 patients with glioblastoma with the selection of morphometric cell clusters. Also, cells of each cluster were isolated, and their proliferative, migratory, invasive activity, survival ability, aerobic glycolysis activity, and chemo- and radioresistance were studied.Four morphometric clusters were identified, including small-cell cluster, paracirculonuclear cluster, hypochromic cluster, and macronuclear cluster, which significantly differed in morphometric parameters and functional parameters. Hypochromic cluster cells demonstrated the highest proliferation activity; macronuclear cluster was the most active glucose consumer; paracirculonuclear cluster had the most prominent migratory and invasive activity and hypoxia resistance; small-cell cluster demonstrated predominantly average values of all parameters. Moreover, additional analysis revealed the presence of a separate subcluster of stem cell elements that correspond in their molecular properties to glioma stem cells and are present in all four clusters. It also turned out that several key molecular parameters of glioblastoma, such as mutational modifications in the EGFR, PDGFRA, and NF1 genes, along with the molecular GBM subtype, are significantly correlated with the identified cell clusters.Thus, the results represent an up-and-coming innovation in the practical field of digital pathology and fundamental questions of glioma carcinogenesis.Copyright © 2023. Published by Elsevier Ltd.