FLUORESCENCE DETECTION OF PAPILLARY TUMOR AT CYSTOSCOPY

膀胱镜检查研究的目的是利用内源性荧光 无染料（自体荧光）光谱法诊断正常粘膜 膀胱镜检查时发现乳头状肿瘤 在过去的一年里，我们使用 采用基于主成分的数据分析新方法 分析（PCA），然后是最小二乘回归（LR）。 的十 使用400 nm激发、校准集和 验证组随机形成，每组5名患者。 分析 PCA和LR的敏感性和特异性分别为100%和100%。 验证集。 400 nm激发数据的单变量分析 为验证集提供了100%的灵敏度和特异性。 对于具有370 nm激发的11名患者， 验证集随机形成6名和5名患者 分别 PCA和LR分析的敏感性分别为90%和93%， 验证集的特异性。 单变量分析结果显示 在90%的灵敏度和93%的特异性的验证。 正常化到患者的正常组织平均值改善了 370 nm激发的结果，但400 nm激发的结果不显著 激发 因此，乳头状瘤可以从正常 粘膜随机分组。 PCA具有以下优点： 从数学上确定光谱的重要部分 而使用的单变量方法需要用户干预， 选择460 nm、600 nm和680 nm发射。

The goal of the cystoscopy study is to use intrinsic fluorescence without dyes (autofluorescence) spectroscopy to diagnose normal mucosa from papillary tumor during cystoscopy. In the past year we are using using new method for data analysis based on principal component analysis (PCA) followed by least-squares regression (LR). For the ten patients taken with 400 nm excitation, a calibration set and a validation set were randomly formed with five patients each. Analysis with PCA and LR gave 100% sensitivity and 100% specificity for the validation set. The univariate analysis for 400 nm excitation data provided sensitivity and specificity of 100% for the validation set. For eleven patients with 370 nm excitation, a calibration set and a validation set were randomly formed with six and five patients respectively. Analysis with PCA and LR gave 90% sensitivity and 93% specificity for the validation set. With univariate anaylsis resulted in 90% sensitivity and 93% spec ificity for the validation. Normalizing to the patients' normal tissue average improved the results for 370 nm excitation but not significantly for 400 nm excitation. Therefore, papillary tumor can be diagnosed from normal mucosa in a randomly divided set. PCA has the advantage of determining mathematically what the important parts of the spectra are while the univariate method used required user intervention and selecting 460 nm, 600, and 680 nm emissions.