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Abstract：
Lung cancer is a leading cause of death worldwide, mostly due to diagnostics in the advanced stage. Therefore, the development of a quick, simple, and non-invasive diagnostic tool to identify cancer is essential. However, the creation of a reliable diagnostic tool is possible only in case of selectivity to other diseases, particularly, cancer of other localizations. This paper is devoted to the study of the variability of exhaled breath samples among patients with lung cancer and cancer of other localizations, such as esophageal, breast, colorectal, kidney, stomach, prostate, cervix, and skin. For this, gas chromatography-mass spectrometry (GC-MS) was used. Two classification models were built. The first model separated patients with lung cancer and cancer of other localizations. The second model classified patients with lung, esophageal, breast, colorectal, and kidney cancer. Mann-Whitney U tests and Kruskal-Wallis H tests were applied to identify differences in investigated groups. Discriminant analysis (DA), gradient-boosted decision trees (GBDT), and artificial neural networks (ANN) were applied to create the models. In the case of classifying lung cancer and cancer of other localizations, average sensitivity and specificity were 68% and 69%, respectively. However, the accuracy of classifying groups of patients with lung, esophageal, breast, colorectal, and kidney cancer was poor.
摘要：
肺癌是全球死亡的主要原因，主要是由于晚期的诊断。因此,一个快速的发展，简单,和非侵入性诊断工具来识别癌症是必不可少的。然而,只有在对其他疾病有选择性的情况下，才有可能创建可靠的诊断工具，特别是,其他地方的癌症。本文致力于研究肺癌和其他定位癌症患者呼出气样本的变异性，比如食道，乳房,结直肠,肾,胃,前列腺,子宫颈,和皮肤。为此，使用气相色谱-质谱(GC-MS)。建立了两种分类模型。第一个模型将肺癌患者和其他定位癌症患者分开。第二个模型对患者进行肺部分类，食道,乳房,结直肠,还有肾癌.采用Mann-WhitneyU检验和Kruskal-WallisH检验来确定研究组的差异。判别分析(DA)，梯度提升决策树(GBDT)，并应用人工神经网络(ANN)来创建模型。在对肺癌和其他定位癌症进行分类的情况下，平均灵敏度和特异度分别为68%和69%，分别。然而,肺癌患者分组分类的准确性，食道,乳房,结直肠,肾癌很差。
