Temerdashev Azamat Z, Gashimova Elina M, Porkhanov Vladimir A, Polyakov Igor S, Perunov Dmitry V, Dmitrieva Ekaterina V
Department of Analytical Chemistry, Kuban State University, Stavropol'skaya St. 149, Krasnodar 350040, Russia.
Research Institute-Regional Clinical Hospital N° 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar 350086, Russia.
Metabolites. 2023 Jan 30;13(2):203. doi: 10.3390/metabo13020203.
Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.
用于肺癌诊断的非侵入性、简单且快速的检测方法是临床实践的迫切需求之一。这项工作描述了使用气相色谱 - 质谱联用仪（GC-MS）对112名肺癌患者和120名健康个体进行呼气分析的结果。数据分析考虑了挥发性有机化合物（VOC）的峰面积及其比率。研究了不同组织学类型、肿瘤定位、TNM分期和治疗状态患者的VOC谱。首次研究了非肺部合并症（慢性心力衰竭、高血压、贫血、急性脑血管意外、肥胖、糖尿病）对肺癌患者呼气成分的影响。发现了一些VOC峰面积及其比率与这些因素之间的显著相关性。使用梯度提升决策树（GBDT）和人工神经网络（ANN）创建了诊断模型。比较了所开发模型的性能。ANN模型最为准确：对测试数据的灵敏度为82 - 88%，特异性为80 - 86%。
