Dipartimento di Ingegneria Elettrica ed Elettronica, Università degli Studi di Cagliari, 09123 Cagliari, Italy.
Laboratorio di Proteomica, Centro Europeo di Ricerca sul Cervello, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy.
Sensors (Basel). 2024 Aug 20;24(16):5383. doi: 10.3390/s24165383.
Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent years, new reports have reinforced the role of an accurate histopathological analysis of carotid plaques to perform the stratification of affected patients and proceed to the correct prevention of complications. This work proposes applying an unsupervised learning approach to analyze complex whole-slide images (WSIs) of atherosclerotic carotid plaques to allow a simple and fast examination of their most relevant features. All the code developed for the present analysis is freely available. The proposed method offers qualitative and quantitative tools to assist pathologists in examining the complexity of whole-slide images of carotid atherosclerotic plaques more effectively. Nevertheless, future studies using supervised methods should provide evidence of the correspondence between the clusters estimated using the proposed textural-based approach and the regions manually annotated by expert pathologists.
中风是全球范围内的第二大致死原因和主要致残原因，颈动脉粥样硬化斑块的发展通常被认为是严重脑血管事件的主要原因。近年来，新的报告强调了对颈动脉斑块进行准确的组织病理学分析以对受影响的患者进行分层并正确预防并发症的作用。这项工作提出应用无监督学习方法来分析动脉粥样硬化颈动脉斑块的复杂全幻灯片图像 (WSI)，以实现对其最相关特征的简单快速检查。目前分析中开发的所有代码都是免费提供的。所提出的方法提供了定性和定量工具，以帮助病理学家更有效地检查颈动脉粥样硬化斑块全幻灯片图像的复杂性。然而，使用监督方法的未来研究应该提供证据，证明使用基于纹理的方法估计的聚类与专家病理学家手动标记的区域之间的对应关系。
