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Methods for autofluorescence analysis of uterine cavity washings

机译:子宫腔清洗液自发荧光分析方法

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摘要

The diagnostics of endometrial pathology can be done by obtaining information at the molecular level, e. g. using the autofluorescence-based technique. Thus, for the first time an experimental study was performed on waste material of uterine cavity washing specimens to evaluate suitability of the technique for diagnostics. The specimens were obtained from 32 patients who had a conventional uterine hydrosonography procedure. A portable Nd:YAG 355 nm microlaser was used to excite autofluorescence at the point of care. Various algorithms of multivariate curve resolution and artificial neural networks were utilized for spectra analysis. The spectra were classified according to histological and ultrasound diagnosis. Receiver operating characteristic (ROC) curve analysis was used to make statistical decisions. The results showed that it was possible to distinguish all compared groups: pathologic vs non-pathologic endometrium (sensitivity 97.3 +/- 5.2%, specificity 91.7 +/- 7%, AUC (area under the ROC curve) 0.96 +/- 0.04), malignant endometrium vs endometrial polyps (sensitivity 100 +/- 0%, specificity 92.0 +/- 10.6%, AUG = 0.98 +/- 0.07), and secretory menstrual cycle phase vs proliferative phase (sensitivity 87.5 +/- 13.2%, specificity 94.4 +/- 7.4%, AUG = 0.88 +/- 0.10). To conclude, uterine cavity washing specimens could be used for endometrial pathology recognition using the autofluorescence-based technique in clinical setting. It will possibly speed up the treatment decision making for endometrial pathology.
机译:子宫内膜病理学的诊断可以通过获得分子水平的信息来完成。 G。使用基于自发荧光的技术。因此,首次对子宫腔冲洗标本的废料进行了实验研究,以评估该技术用于诊断的适用性。标本取自32例接受常规子宫超声检查的患者。在护理时,使用便携式Nd:YAG 355 nm微激光器激发自发荧光。利用多种变量曲线解析算法和人工神经网络进行光谱分析。根据组织学和超声诊断对光谱进行分类。接收器工作特性(ROC)曲线分析用于做出统计决策。结果表明,有可能区分所有比较的组:病理性子宫内膜与非病理性子宫内膜(敏感性为97.3 +/- 5.2%,特异性为91.7 +/- 7%,AUC(ROC曲线下的面积)为0.96 +/- 0.04) ,恶性子宫内膜vs子宫内膜息肉(敏感性100 +/- 0%,特异性92.0 +/- 10.6%,AUG = 0.98 +/- 0.07),月经周期分泌相与增生期(敏感性87.5 +/- 13.2%,特异性94.4 +/- 7.4%,AUG = 0.88 +/- 0.10)。总之,子宫腔冲洗标本可以在临床环境中使用基于自发荧光的技术用于子宫内膜病理识别。它可能会加快子宫内膜病理的治疗决策。

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