Dieterle Frank, Müller-Hagedorn Silvia, Liebich Hartmut M, Gauglitz Günter
Institute of Physical and Theoretical Chemistry, Auf der Morgenstelle 8, D-72076 Tübingen, Germany.
Artif Intell Med. 2003 Jul;28(3):265-79. doi: 10.1016/s0933-3657(03)00058-7.
Modified nucleosides were recently presented as potential tumor markers for breast cancer. The patterns of the levels of urinary nucleosides are different for tumor bearing individuals and for healthy individuals. Thus, a powerful pattern recognition method is needed. Although backpropagation (BP) neural networks are becoming increasingly common in medical literature for pattern recognition, it has been shown that often-superior methods exist like learning vector quantization (LVQ) and support vector machines (SVM). The aim of this feasibility study is to get an indication of the performance of urinary nucleoside levels evaluated by LVQ in contrast to the evaluation the popular BP and SVM networks. Urine samples were collected from female breast cancer patients and from healthy females. Twelve different ribonucleosides were isolated and quantified by a high performance liquid chromatography (HPLC) procedure. LVQ, SVM and BP networks were trained and the performance was evaluated by the classification of the test sets into the categories "cancer" and "healthy". All methods showed a good classification with a sensitivity ranging from 58.8 to 70.6% at a specificity of 88.4-94.2% for the test patterns. Although the classification performance of all methods is comparable, the LVQ implementations are superior in terms of more qualitative features: the results of LVQ networks are more reproducible, as the initialization is deterministic. The LVQ networks can be trained by unbalanced sizes of the different classes. LVQ networks are fast during training, need only few parameters adjusted for training and can be retrained by patterns of "local individuals". As at least some of these features play an important role in an implementation into a medical decision support system, it is recommended to use LVQ for an extended study.
修饰核苷最近被认为是乳腺癌潜在的肿瘤标志物。荷瘤个体和健康个体尿液核苷水平的模式有所不同。因此，需要一种强大的模式识别方法。尽管反向传播（BP）神经网络在医学文献中用于模式识别越来越普遍，但已表明存在一些通常更优的方法，如学习向量量化（LVQ）和支持向量机（SVM）。本可行性研究的目的是了解与常用的BP和SVM网络相比，LVQ评估尿液核苷水平的性能。从女性乳腺癌患者和健康女性中收集尿液样本。通过高效液相色谱（HPLC）程序分离并定量了12种不同的核糖核苷。对LVQ、SVM和BP网络进行了训练，并通过将测试集分类为“癌症”和“健康”类别来评估性能。对于测试模式，所有方法都显示出良好的分类效果，灵敏度范围为58.8%至70.6%，特异性为88.4%至94.2%。尽管所有方法的分类性能相当，但LVQ实现方式在更多定性特征方面更具优势：LVQ网络的结果更具可重复性，因为初始化是确定性的。LVQ网络可以用不同类别的不平衡规模进行训练。LVQ网络在训练过程中速度快，训练时只需调整很少的参数，并且可以通过“局部个体”的模式进行重新训练。由于这些特征中的至少一些在医学决策支持系统的实现中起着重要作用，建议使用LVQ进行进一步研究。
