Cyranose 320电子鼻运用SVM算法检测呼出气体诊断肺癌研究
Detection of lung cancer in exhaled breath with an electronic nose using support vector machine analysis电子鼻运用SVM算法检测呼出气体诊断肺癌研究
Madara Tirzīte1,2,Māris Bukovskis1, Gunta Strazda1, Normunds Jurka1 and Immanuels Taivans1 1 Faculty of Medicine, University of Latvia, Raina Blv. 19, Riga, Latvia 2 Riga East Clinical University Hospital ‘Gailezers’, 2nd Department of Pulmonology, Hipokrata Str. 2, Riga, Latvia : madara.tirzite@aslimnica.lv Keywords: lung cancer, electronic nose, support vector machine, SVM, E-nose, artificial olfactory sensor
Abstract Lung cancer is one of the most common malignancies and has a low 5-year survival rate. There are no cheap, simple and widely available screening methods for the early diagnostics of lung cancer. The aim of this study was to determine whether analysis of exhaled breath with an artificial olfactory sensor using support vector analysis can differentiate patients with lung cancer from healthy individuals and patients with other lung diseases, regardless of the stage of lung cancer and the most common comorbidities. Patients with histologically or cytologically verified lung cancer, healthy volunteers and patients with other lung diseases (e.g. chronic obstructive pulmonary disease (COPD), asthma, pneumonia, pulmonary embolism, benign lung tumors) were enrolled in the study. Breath sample collection and analysis with a Cyranose 320 sensor device was performed and data were further analyzed using a support vector machine (SVM). TheSVMcorrectly differentiated between cancer patients and healthy volunteers in 98.8% of cases. The cancer versus non-cancer group patients (healthy volunteers and patients with other lung diseases) were classified correctly bySVMin 87.3% of cases. In the mixed diagnosis groups (only cancer, only COPD, cancer+COPDand control) all 79 out of 79 patients were predicted correctly in the cancer+COPDgroup, with the rate of correct prognosis in other patient groups being lower. Exhaled breath analysis by electronic nose using aSVMis able to discriminate patients with lung cancer from healthy subjects and mixed groups of patients with different lung diseases. It can also provide a certain level of discrimination between lung cancer patients, lung cancer patients with concomitant COPD,COPDalone and a healthy control group.
肺癌是见的恶性肿瘤之一，5年生存率低。目前还没有廉价、简单和广泛使用的肺癌早期诊断筛查方法。本研究的目的是确定无论肺癌的分期和见的合并症如何，使用支持向量分析的人工嗅觉传感器分析呼出的呼吸是否能区分肺癌患者与健康人和其他肺部疾病患者。对经组织学或细胞学证实的肺癌患者、健康志愿者和其他肺部疾病（如慢性阻塞性肺病（COPD）、哮喘、肺炎、肺栓塞、良性肺肿瘤）患者进行研究。采用Cyranose 320传感器对呼吸样本进行采集和分析，并利用支持向量机（SVM）对数据进行进一步分析。在98.8%的病例中，肿瘤患者和健康志愿者的svmv值有正确的差异。癌症组与非癌症组患者（健康志愿者和其他肺部疾病患者）按svmin 87.3%正确分类。在混合诊断组（仅癌症组、仅慢性阻塞性肺病组、癌症+慢性阻塞性肺病对照组）中，79例患者中79例在癌症+慢性阻塞性肺病组中预测正确，其他患者组的正确预后率较低。利用ASVMIS进行电子鼻呼气分析，可以将肺癌患者与健康受试者以及不同肺部疾病患者的混合组进行区分。它还可以在肺癌患者、伴慢性阻塞性肺病的肺癌患者、copdalone和健康对照组之间提供一定程度的鉴别。
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