Cervical Cancer Prognosis and Diagnosis Using Electrical Impedance Spectroscopy.
Cervical Cancer Prognosis and Diagnosis Using Electrical Impedance Spectroscopy.
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DOI：
10.2478/joeb-2021-0018
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发表时间：
2021-01
影响因子：
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通讯作者：
Kell D
中科院分区：
文献类型：
--
作者：
Li P;Highfield PE;Lang ZQ;Kell D
电阻抗谱（EIS）已被用作阴道镜检查的辅助手段用于宫颈癌诊断多年。目前，模板匹配方法用于EIS测量分析，其中将测量的EIS谱与从癌性和非癌性宫颈组织的三维有限元（FE）模型生成的模板进行比较，然后使用测量的EIS光谱和模板之间的匹配来导出指示测量的EIS与高度宫颈上皮内瘤变（HG CIN）的关联强度的分数。这些FE模型可以被视为相关物理组织模型的计算版本。在本文中，这个问题是重新审视的目标，开发一种新的方法，EIS数据分析，可能会揭示之间的关系，由于疾病的组织结构的变化和测量光谱的变化。这将为我们了解基于EIS的HG CIN诊断决策的组织病理学机制以及EIS对宫颈癌诊断的预后价值提供重要信息。另一个目的是开发一种用于HG CIN检测的替代EIS数据处理方法，其不依赖于组织的物理模型，以便促进将EIS技术扩展到模板光谱不可用的新的医学诊断应用。为实现上述目标，本文提出了一种基于EIS数据驱动的方法，将EIS数据分析用于宫颈癌诊断和预后的问题转化为分类问题，提出了一种基于科尔模型的谱曲线拟合方法，从EIS数据中提取特征进行分类。然后使用机器学习技术来构建具有所选特征的分类模型，用于宫颈癌诊断和评估测量的EIS的预后价值。可解释的分类模型是用真实的EIS数据集开发的，这使我们能够将观察到的EIS的变化和HG CIN或发展HG CIN的风险与疾病引起的组织结构变化相关联。将所建立的分类模型用于HG CIN的检测和EIS预后价值的评估，结果证明了所建立方法的有效性。开发的方法是长期受益的EIS为基础的宫颈癌诊断，并结合标准阴道镜检查，有可能为开发的方法提供一个更有效和更高效的患者管理策略的临床实践。
Electrical impedance spectroscopy (EIS) has been used as an adjunct to colposcopy for cervical cancer diagnosis for many years, Currently, the template match method is employed for EIS measurements analysis, where the measured EIS spectra are compared with the templates generated from three-dimensional finite element (FE) models of cancerous and non-cancerous cervical tissue, and the matches between the measured EIS spectra and the templates are then used to derive a score that indicates the association strength of the measured EIS to the High-Grade Cervical Intraepithelial Neoplasia (HG CIN). These FE models can be viewed as the computational versions of the associated physical tissue models. In this paper, the problem is revisited with an objective to develop a new method for EIS data analysis that might reveal the relationship between the change in the tissue structure due to disease and the change in the measured spectrum. This could provide us with important information to understand the histopathological mechanism that underpins the EIS-based HG CIN diagnostic decision making and the prognostic value of EIS for cervical cancer diagnosis. A further objective is to develop an alternative EIS data processing method for HG CIN detection that does not rely on physical models of tissues so as to facilitate extending the EIS technique to new medical diagnostic applications where the template spectra are not available. An EIS data-driven method was developed in this paper to achieve the above objectives, where the EIS data analysis for cervical cancer diagnosis and prognosis were formulated as the classification problems and a Cole model-based spectrum curve fitting approach was proposed to extract features from EIS readings for classification. Machine learning techniques were then used to build classification models with the selected features for cervical cancer diagnosis and evaluation of the prognostic value of the measured EIS. The interpretable classification models were developed with real EIS data sets, which enable us to associate the changes in the observed EIS and the risk of being HG CIN or developing HG CIN with the changes in tissue structure due to disease. The developed classification models were used for HG CIN detection and evaluation of the prognostic value of EIS and the results demonstrated the effectiveness of the developed method. The method developed is of long-term benefit for EIS–based cervical cancer diagnosis and, in conjunction with standard colposcopy, there is the potential for the developed method to provide a more effective and efficient patient management strategy for clinic practice.