Zhang Honghao, Hang Jiu-Tao, Chang Zhuo, Yu Suihuai, Yang Hui, Xu Guang-Kui
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China.
Department of Engineering Mechanics, SVL, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, China.
Front Bioeng Biotechnol. 2024 Jul 16;12:1404508. doi: 10.3389/fbioe.2024.1404508. eCollection 2024.
Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues.
细胞和组织力学研究表明，在损伤和癌症过程中可观察到细胞和组织力学的显著变化，这为基于机器学习的疾病诊断提供了新的力学标志物。然而，由于缺乏有效的力学标志物，目前仅将弹性模量和图像特征用作标志物，这极大地限制了细胞和组织力学在疾病诊断中的应用。在此，我们基于高维粘弹性力学数据，通过支持向量机方法开发了一种肝脏病理状态分类器。肝纤维化的准确诊断和分级有助于早期检测和治疗，并可为药物开发提供评估工具。为此，我们使用通过自相似层次模型对肝脏组织蠕变响应分析获得的粘弹性参数，并基于机器学习构建了肝脏状态分类器。使用该分类器，我们实现了对健康、患病和间充质干细胞（MSC）处理的纤维化肝脏组织的快速分类，我们的结果表明健康肝脏和患病肝脏的分类准确率可达0.99，三种肝脏组织混合的分类准确率也达到0.82。最后，我们基于特征消融提供了在海量数据以及高维粘弹性变量背景下的标志物筛选方法，用于药物开发和肝纤维化的准确分级。我们提出了一种新颖的分类器，它使用动态力学变量作为输入标志物，可识别健康、患病和治疗后的肝脏组织。
