H. Tyagi, E. Daulton, A. Bannaga, R. Arasaradnam, J. Covington
{"title":"电子鼻用于膀胱癌检测","authors":"H. Tyagi, E. Daulton, A. Bannaga, R. Arasaradnam, J. Covington","doi":"10.3390/csac2021-10438","DOIUrl":null,"url":null,"abstract":"This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.","PeriodicalId":9815,"journal":{"name":"Chemistry Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Electronic Nose for Bladder Cancer Detection\",\"authors\":\"H. Tyagi, E. Daulton, A. Bannaga, R. Arasaradnam, J. Covington\",\"doi\":\"10.3390/csac2021-10438\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.\",\"PeriodicalId\":9815,\"journal\":{\"name\":\"Chemistry Proceedings\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemistry Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/csac2021-10438\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemistry Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/csac2021-10438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
本研究概述了使用电子鼻作为一种方法来检测挥发性有机化合物作为膀胱癌的生物标志物。本文使用AlphaMOS FOX 4000电子鼻对15例膀胱癌患者和41例非膀胱癌患者的尿液样本进行了分析。FOX 4000由18个MOS传感器组成,用于区分两组。使用MultiSens Analyzer和RStudio对所得结果进行分析。结果表明,使用稀疏逻辑回归和随机森林分类器分别具有0.93和0.88和0.93和0.76的高分离度和特异性。我们的结论是,电子鼻显示出利用尿液样本区分膀胱癌和非膀胱癌受试者的潜力。
This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.