WestCHEM, Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, Glasgow, UK.
Neuropathology, Lancashire Teaching Hospitals NHS Trust, Royal Preston Hospital, Preston, UK.
J Biophotonics. 2020 Sep;13(9):e202000118. doi: 10.1002/jbio.202000118. Epub 2020 Jun 23.
In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.
近年来，人们研究了利用衰减全反射傅里叶变换红外光谱（ATR-FTIR）对干燥的人血清样本进行脑肿瘤诊断，以消除水成分的光谱干扰，取得了有前景的结果。本研究评估了ATR-FTIR 在液体和风干样本上的应用，以研究“数字干燥”作为分析液体样本光谱的替代方法。数字干燥方法，包括水扣除和最小二乘法，在区分癌症与对照样本时，与风干光谱方法相比，表现出更高的随机森林（RF）分类性能，达到了高于 93.0%的灵敏度值和高于 83.0%的特异性值。此外，利用基于量子级联激光红外（QCL-IR）的光谱成像技术对液体样本进行评估，以研究深穿透光源对疾病分类的影响。RF 对 QCL-IR 数据的分类提供了 85.1%和 75.3%的灵敏度和特异性。
