Digital rectal examination (DRE) was the primary means to detect prostate diseases. The DRE has a high variability as it is based manly in the tactile sensitivity and expertise of the examiner. The prostate-specific antigen (PSA) test was initially developed for surveillance of prostate cancer and later it was also used as a diagnosis test. Raman spectroscopy is a powerful analytical technique that can measure the chemical composition of complex biological samples, such as body fluids. Biochemical changes caused by diseases can lead to significant changes in the Raman spectra. This study aimed to identify the differences in the Raman spectra of serum samples with normal and altered PSA values and correlate these differences by using multivariate techniques (principal component analysis - PCA and partial least squares regression - PLS). A total of 321 spectra were collected from 108 subjects. Two hundred and seventy were obtained from 91 non-altered PSA samples and 51 spectra from 17 samples with altered PSA. Each spectrum acquired was standardized to the area under the curve (1-norm). Discriminating and quantitative models employing PCA and PLS were developed. The PCA analyses showed 85.7% predictive power (87.41% sensitivity and 76.47% specificity). The PLS test showed a near-perfect sensitivity (98.51%) and an intermediate specificity (62.75%). The quantitative model through PLS regression showed a good correlation between PSA values and the spectral features (r = 0.605). This preliminary study suggests that Raman spectroscopy could be efficiently used for screening patients with altered PSA as well as for follow-up of the treatment of the prostate cancer by using initially the PLS to identify the possible presence of the prostate cancer and later on use de PCA to confirm the diagnosis.

中文翻译：

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拉曼光谱法检测前列腺癌：PSA值正常和变化的患者的多变量研究。

直肠指检（DRE）是检测前列腺疾病的主要手段。DRE具有很大的可变性，因为它主要基于检查者的触觉敏感性和专业知识。前列腺特异性抗原（PSA）测试最初是为监视前列腺癌而开发的，后来也被用作诊断测试。拉曼光谱法是一种强大的分析技术，可以测量复杂生物样品（例如体液）的化学成分。疾病引起的生化变化可能导致拉曼光谱发生重大变化。这项研究旨在鉴定具有正常和改变的PSA值的血清样品的拉曼光谱差异，并通过使用多元技术（主成分分析-PCA和偏最小二乘回归-PLS）将这些差异关联起来。从108个受试者中收集了321个光谱。从91个未更改的PSA样本中获得了270个，从17个具有更改后的PSA的样本中获得了51个光谱。将获取的每个光谱标准化到曲线下的区域（1-范数）。建立了采用PCA和PLS的判别和定量模型。PCA分析显示出85.7％的预测能力（87.41％的敏感性和76.47％的特异性）。PLS测试显示接近完美的灵敏度（98.51％）和中等特异性（62.75％）。通过PLS回归的定量模型显示PSA值与光谱特征之间具有良好的相关性（r = 0.605）。