Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia.
Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, 173003 Veliky Novgorod, Russia.
Biomolecules. 2023 Aug 29;13(9):1327. doi: 10.3390/biom13091327.
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.
微血管分析和血管生成评估在各种疾病中具有重要的预后价值，包括癌症。寻找血管侵犯和评估血管生成是肿瘤学诊断的重要方面。这些特征决定了肿瘤的预后和侵袭性。传统的手动评估方法既耗时又容易受到观察者间的变异性的影响。血管检测是人工智能的完美任务，它能够快速分析全幻灯片图像中的数千个组织结构。计算机视觉解决方案的开发需要对组织区域进行分割、提取特征和训练机器学习模型。在这篇综述中，我们重点介绍了研究人员用于识别各种肿瘤定位（包括乳腺癌、肺癌、结肠癌、脑癌、肾癌、胰腺癌、胃癌和口腔癌）中的血管和血管侵犯的方法。当代模型预示着形态学诊断中计算病理学的新时代。
