傅里叶变换红外光谱法、化学计量学和机器学习方法用于尿液分析以确定尿液中前列腺癌的光谱标记物
Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.
作者信息
Mitura Przemysław, Paja Wiesław, Klebowski Bartosz, Płaza Paweł, Bar Krzyszof, Młynarczyk Grzegorz, Depciuch Joanna
机构信息
Department of Urology and Oncological Urology, Medical University of Lublin, Lublin, Poland.
Department of Artificial Intelligence, Institute of Computer Science, University of Rzeszow, Rzeszów, Poland.
出版信息
J Biophotonics. 2025 Jan;18(1):e202400278. doi: 10.1002/jbio.202400278. Epub 2024 Nov 21.
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 and 2972 cm were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category.
前列腺特异性抗原（PSA）是前列腺癌最常用的标志物。然而，近25%的PSA水平升高的男性没有患癌，近20%的前列腺癌患者血清PSA水平正常。因此，在本研究中，傅里叶变换红外（FTIR）光谱被作为一种从尿液中检测前列腺癌的新工具进行了研究。获得的结果显示，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）未发现区分健康和非健康患者尿液的可能性。然而，机器学习算法显示FTIR从尿液中检测前列腺癌的准确率和精确率为0.90。我们发现1614 cm和2972 cm处的波数是前列腺癌光谱标志物的候选者。重要的是，这些FTIR标志物与Gleason评分、PSA和mpMRI PI-RADS类别相关。
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