利用傅立叶变换红外光谱、化学计量学和机器学习方法分析尿液,确定尿液中前列腺癌的光谱标记。

Przemysław Mitura, Wiesław Paja, Bartosz Klebowski, Paweł Płaza, Krzyszof Bar, Grzegorz Młynarczyk, Joanna Depciuch
{"title":"利用傅立叶变换红外光谱、化学计量学和机器学习方法分析尿液,确定尿液中前列腺癌的光谱标记。","authors":"Przemysław Mitura, Wiesław Paja, Bartosz Klebowski, Paweł Płaza, Krzyszof Bar, Grzegorz Młynarczyk, Joanna Depciuch","doi":"10.1002/jbio.202400278","DOIUrl":null,"url":null,"abstract":"<p><p>Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine. Obtained results showed higher levels of glucose, urea and creatinine in urine collected from patients with prostate cancer than that in control. Principal component analysis (PCA) was not noticed possibility of differentiation urine collected from healthy and nonhealthy patients. However, machine learning algorithms showed 0.90 accuracy and precision of FTIR in detection of prostate cancer from urine. We showed that wavenumbers at 1614 cm<sup>-1</sup> and 2972 cm<sup>-1</sup> were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category.</p>","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202400278"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.\",\"authors\":\"Przemysław Mitura, Wiesław Paja, Bartosz Klebowski, Paweł Płaza, Krzyszof Bar, Grzegorz Młynarczyk, Joanna Depciuch\",\"doi\":\"10.1002/jbio.202400278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine. Obtained results showed higher levels of glucose, urea and creatinine in urine collected from patients with prostate cancer than that in control. Principal component analysis (PCA) was not noticed possibility of differentiation urine collected from healthy and nonhealthy patients. However, machine learning algorithms showed 0.90 accuracy and precision of FTIR in detection of prostate cancer from urine. We showed that wavenumbers at 1614 cm<sup>-1</sup> and 2972 cm<sup>-1</sup> were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category.</p>\",\"PeriodicalId\":94068,\"journal\":{\"name\":\"Journal of biophotonics\",\"volume\":\" \",\"pages\":\"e202400278\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of biophotonics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/jbio.202400278\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biophotonics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/jbio.202400278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

前列腺特异性抗原(PSA)是最常用的前列腺癌标志物。然而,近 25% PSA 水平升高的男性并未罹患癌症,近 20% 的前列腺癌患者血清 PSA 水平正常。因此,本研究将傅立叶变换红外光谱(FTIR)作为从尿液中检测前列腺癌的一种新工具进行研究。结果显示,前列腺癌患者尿液中的葡萄糖、尿素和肌酐水平高于对照组。主成分分析(PCA)无法区分健康和非健康患者的尿液。不过,机器学习算法显示,傅立叶变换红外光谱从尿液中检测前列腺癌的准确度和精确度均为 0.90。我们发现 1614 cm-1 和 2972 cm-1 波长是前列腺癌光谱标记的候选波长。重要的是,这些傅立叶变换红外标记与格里森评分、PSA 和 mpMRI PI-RADS 类别相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.

Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine. Obtained results showed higher levels of glucose, urea and creatinine in urine collected from patients with prostate cancer than that in control. Principal component analysis (PCA) was not noticed possibility of differentiation urine collected from healthy and nonhealthy patients. However, machine learning algorithms showed 0.90 accuracy and precision of FTIR in detection of prostate cancer from urine. We showed that wavenumbers at 1614 cm-1 and 2972 cm-1 were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信