Diagnosis of breast cancer with infrared spectroscopy from serum samples
Diagnosis of breast cancer with infrared spectroscopy from serum samples
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DOI：
10.1016/j.vibspec.2010.01.013
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发表时间：
2010-03-18
影响因子：
2.5
通讯作者：
Bugert, Peter
中科院分区：
文献类型：
--
作者：
Backhaus, Juergen;Mueller, Ralf;Bugert, Peter
乳腺癌的检测在癌症疾病的诊断中具有特殊的价值。它是女性中最常见的癌症类型。我们已经开发了一种简单而快速的方法，用于检测乳腺癌的红外光谱。该方法仅需1 μ l血清样品。将血清样品在合适的样品载体如Si板上干燥。干燥后，测量IR光谱。每种疾病都会在血清的红外光谱中留下典型的指纹。这种典型的指纹可以用来识别不同的患者群体。识别系统可以通过分类方法进行训练。我们使用了两个独立的分类方法，聚类分析和人工神经网络（ANN）。该研究涉及196名患者。通过聚类分析（一种无监督学习方法），我们实现了98%的灵敏度和95%的特异性。ANN（一种监督学习方法）的灵敏度为92%，特异性为100%。为了确保我们不会对其他疾病产生任何干扰，乳腺癌患者分别对其他11种疾病进行了测试。共有3119人参加了这项研究。标准是有多少患者被分配到正确的组。91%的患者被分配到右侧组。乳腺癌被分配到79%的正确组。这些结果表明，红外光谱结合智能数学评价工具，如人工神经网络或聚类分析是一个很好的工具，用于诊断乳腺癌。(C)2010爱思唯尔有限公司版权所有。
The detection of breast cancer has a special value in the diagnosis of cancer diseases. It is the most frequent type of cancer among women's. We have developed a simple and rapid method for the detection of breast cancer with IR-spectroscopy. The method needs only 1 mu l of a serum sample. The serum sample is dried on a suitable sample carrier such as a Si-plate. After drying the IR-spectrum is measured. Every disease leaves a typical fingerprint in the IR-spectrum of serum. This typical fingerprint can be used to identify different patient groups. The identification system can be trained by classification methods. We used two independent classification methods, cluster analysis and artificial neural networks (ANN). The study was carried out with 196 patients. With cluster analysis (a method of unsupervised learning) we achieved a sensitivity of 98% and a specificity of 95%. With ANN (a method of supervised learning) sensitivity of 92% and specificity of 100% was being determined. To sure that we do not have any interference with other diseases the breast cancer patients tested against 11 other diseases separately. Altogether, 3119 people took part in the study. The criterion was how many patients were assigned to the right group. 91% of all patients were assigned to the right group. Breast cancer was assigned to 79% to the correct group. These results suggest that IR-spectroscopy in combination with intelligent mathematical evaluation tools such as ANN or cluster analysis is a good tool for the diagnosis of breast cancer. (C) 2010 Elsevier B.V. All rights reserved.