Institute of Nuclear Sciences Vinča, University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia.
Appl Spectrosc. 2011 Mar;65(3):293-7. doi: 10.1366/10-05928.
Data from total synchronous fluorescence spectroscopy (TSFS) measurements of normal and malignant breast tissue samples are introduced in supervised self-organizing maps, a type of artificial neural network (ANN), to obtain diagnosis. Three spectral regions in both TSFS patterns and first-derivative TSFS patterns exhibited clear differences between normal and malignant tissue groups, and intensities measured from these regions served as inputs to neural networks. Histology findings are used as the gold standard to train self-organizing maps in a supervised way. Diagnostic accuracy of this procedure is evaluated with sample test groups for two cases, when the neural network uses TSFS data and when the neural network uses data from first-derivative TSFS. In the first case diagnostic sensitivity of 87.1% and specificity of 91.7% are found, while in the second case sensitivity of 100% and specificity of 94.4% are achieved.
本文介绍了应用于监督自组织映射（一种人工神经网络）中的正常和恶性乳腺组织样本的总同步荧光光谱（TSFS）测量数据，以获得诊断。在 TSFS 图谱和一阶导数 TSFS 图谱的三个光谱区域中，正常组织组和恶性组织组之间存在明显差异，这些区域的强度被用作神经网络的输入。组织学发现被用作监督自组织映射训练的金标准。对于两种情况，使用 TSFS 数据和使用一阶导数 TSFS 数据的神经网络，使用样本测试组评估了该过程的诊断准确性。在第一种情况下，诊断灵敏度为 87.1%，特异性为 91.7%，而在第二种情况下，灵敏度为 100%，特异性为 94.4%。
