PDF(Pubmed)
Abstract：
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.
摘要：
细胞和组织力学的研究表明，在病变和癌症期间观察到细胞和组织力学的显着变化，为基于机器学习的疾病诊断提供了新的机械标记。然而,由于缺乏有效的机械标记，目前只有弹性模量和图像特征被用作标记，极年夜限制了细胞和组织力学在疾病诊断中的运用。这里,我们通过支持向量机方法开发了肝脏病理状态分类器，基于高维粘弹性力学数据。肝纤维化的准确诊断和分级有助于早期检测和治疗，并可能为药物开发提供评估工具。为此,我们使用自相似分层模型分析肝脏组织蠕变响应获得的粘弹性参数，并建立了基于机器学习的肝脏状态分类器。使用这个分类器，我们实施了健康快速分类，患病,和间充质干细胞（MSC）处理的纤维化活组织，我们的结果表明，健康和患病肝脏的分类精度可以达到0.99，三种混合肝脏组织的分类精度也达到0.82。最后,我们提供了在海量数据背景下的标志物筛选方法以及基于特征消融的高维粘弹性变量，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，它使用动态机械变量作为输入标记，可以识别健康，患病,和治疗后的肝脏组织。
