Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.

Journal of biophotonics Pub Date : 2024-11-21 DOI:10.1002/jbio.202400278

Przemysław Mitura, Wiesław Paja, Bartosz Klebowski, Paweł Płaza, Krzyszof Bar, Grzegorz Młynarczyk, Joanna Depciuch

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Abstract

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.

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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。

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。

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Journal of biophotonics

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