Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee 37235, USA.
J Biomed Opt. 2011 Nov;16(11):117004. doi: 10.1117/1.3646210.
Many groups have used Raman spectroscopy for diagnosing cervical dysplasia; however, there have been few studies looking at the effect of normal physiological variations on Raman spectra. We assess four patient variables that may affect normal Raman spectra: Race/ethnicity, body mass index (BMI), parity, and socioeconomic status. Raman spectra were acquired from a diverse population of 75 patients undergoing routine screening for cervical dysplasia. Classification of Raman spectra from patients with a normal cervix is performed using sparse multinomial logistic regression (SMLR) to determine if any of these variables has a significant effect. Results suggest that BMI and parity have the greatest impact, whereas race/ethnicity and socioeconomic status have a limited effect. Incorporating BMI and obstetric history into classification algorithms may increase sensitivity and specificity rates of disease classification using Raman spectroscopy. Studies are underway to assess the effect of these variables on disease.
许多研究团队已经使用拉曼光谱学来诊断宫颈发育不良，但很少有研究关注正常生理变化对拉曼光谱的影响。我们评估了四个可能影响正常拉曼光谱的患者变量：种族/民族、体重指数（BMI）、生育状况和社会经济地位。我们从接受常规宫颈发育不良筛查的 75 名患者中采集了拉曼光谱。使用稀疏多项逻辑回归（SMLR）对来自正常宫颈患者的拉曼光谱进行分类，以确定这些变量是否具有显著影响。结果表明，BMI 和生育状况的影响最大，而种族/民族和社会经济地位的影响有限。将 BMI 和产科史纳入分类算法中，可能会提高拉曼光谱检测疾病的灵敏度和特异性。目前正在进行研究以评估这些变量对疾病的影响。
