基于尿液色谱分析的神经网络转移性黑色素瘤检测方法
Neural network approach to detection of metastatic melanoma from chromatographic analysis of urine.
作者信息
Cohen M E, Hudson D L, Banda P W, Blois M S
机构信息
California State University, Fresno.
出版信息
Proc Annu Symp Comput Appl Med Care. 1991:295-9.
Chromatographic analysis of sera or urine is important in medicine for the evaluation of patients whose clinical status is associated with the presence of specific biochemical markers. Malignant melanoma has been a model for such studies due to the elaboration of melanin precursors and pigment as the tumor metastasizes. Computer-assisted methods for categorizing chromatographic data and clinical status are imperative due to the large number of detectable compounds and possible correlations. In addition, computer-based analysis of the data can readily extract patterns that are not obvious by visual inspection. In this paper, we present a neural network analysis of melanoma chromatographic and clinical data that categorizes subjects into normals, NED patients (No Evidence of Disease), and metastatic patients. The set of marker compounds for metastatic disease represents a significant advance over the correlations derived by visual inspection.
血清或尿液的色谱分析在医学上对于评估临床状况与特定生化标志物存在相关的患者非常重要。恶性黑色素瘤一直是此类研究的一个模型，因为随着肿瘤转移会产生黑色素前体和色素。由于可检测化合物数量众多以及可能存在的相关性，用于对色谱数据和临床状况进行分类的计算机辅助方法势在必行。此外，基于计算机的数据分析能够轻松提取通过目视检查不明显的模式。在本文中，我们展示了对黑色素瘤色谱和临床数据的神经网络分析，该分析将受试者分为正常组、无疾病证据（NED）患者组和转移患者组。用于转移性疾病的标志物化合物组相较于目视检查得出的相关性有了显著进步。
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