Landulfo Silveira, Sokki Sathaiah, Renato Amaro Zângaro, Marcos Tadeu Tavares Pacheco, Maria Cristina Chavantes, Carlos Augusto Pasqualucci
{"title":"Near-infrared Raman spectroscopy of human coronary arteries: histopathological classification based on Mahalanobis distance.","authors":"Landulfo Silveira, Sokki Sathaiah, Renato Amaro Zângaro, Marcos Tadeu Tavares Pacheco, Maria Cristina Chavantes, Carlos Augusto Pasqualucci","doi":"10.1089/104454703768247774","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>In this study, near-infrared Raman spectroscopy (NIRS) was used for evaluation of human atherosclerotic lesions using a simple algorithm based on discriminant analysis. The Mahalanobis distance was used to classify the clustered spectral features extracted from NIRS of a total of 111 arterial fragments of human coronary arteries.</p><p><strong>Background data: </strong>Raman spectroscopy has been used for diagnosis of a variety of diseases. For real-time applications, it is important to have a simple algorithm that could perform fast data acquisition and analysis. The ultimate goal is to obtain a feasible diagnosis, which discriminates various atherosclerotic lesions with high sensitivities and specificities.</p><p><strong>Materials and methods: </strong>Non-atherosclerotic (NA) arteries, atherosclerotic plaques without calcification (NC), and atherosclerotic plaques with classification (C) were obtained and scanned with an NIR Raman spectrometer with 830-nm laser excitation. An algorithm based on the discriminant analysis using the Mahalanobis distance of the clustered spectral features was used for tissue classification into three categories: Na, NC, and C.</p><p><strong>Results: </strong>Human coronary arteries exhibit different spectral signatures depending on different bio-chemicals present in each tissue type such as collagen, cholesterol, and calcium hydroxyapatite, respectively. It is shown that our algorithm has a maximum sensitivity and specificity of 85% and 89%, respectively, for the diagnosis of the NA tissue type, 85% and 89% for the NC tissue type, and 100% and 100% for the C tissue type.</p><p><strong>Conclusion: </strong>An algorithm (with a minimum of mathematical and computational requirements) based on the discriminant analysis of spectral features has been developed to classify atherosclerotic lesions with high sensitivities and specificities.</p>","PeriodicalId":79503,"journal":{"name":"Journal of clinical laser medicine & surgery","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2003-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1089/104454703768247774","citationCount":"36","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of clinical laser medicine & surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1089/104454703768247774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 36
Abstract
Objective: In this study, near-infrared Raman spectroscopy (NIRS) was used for evaluation of human atherosclerotic lesions using a simple algorithm based on discriminant analysis. The Mahalanobis distance was used to classify the clustered spectral features extracted from NIRS of a total of 111 arterial fragments of human coronary arteries.
Background data: Raman spectroscopy has been used for diagnosis of a variety of diseases. For real-time applications, it is important to have a simple algorithm that could perform fast data acquisition and analysis. The ultimate goal is to obtain a feasible diagnosis, which discriminates various atherosclerotic lesions with high sensitivities and specificities.
Materials and methods: Non-atherosclerotic (NA) arteries, atherosclerotic plaques without calcification (NC), and atherosclerotic plaques with classification (C) were obtained and scanned with an NIR Raman spectrometer with 830-nm laser excitation. An algorithm based on the discriminant analysis using the Mahalanobis distance of the clustered spectral features was used for tissue classification into three categories: Na, NC, and C.
Results: Human coronary arteries exhibit different spectral signatures depending on different bio-chemicals present in each tissue type such as collagen, cholesterol, and calcium hydroxyapatite, respectively. It is shown that our algorithm has a maximum sensitivity and specificity of 85% and 89%, respectively, for the diagnosis of the NA tissue type, 85% and 89% for the NC tissue type, and 100% and 100% for the C tissue type.
Conclusion: An algorithm (with a minimum of mathematical and computational requirements) based on the discriminant analysis of spectral features has been developed to classify atherosclerotic lesions with high sensitivities and specificities.