Handa Hiroshi, Usuba Ayano, Maddula Sasidhar, Baumbach Jörg Ingo, Mineshita Masamichi, Miyazawa Teruomi
Division of Respiratory and Infectious Diseases, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki-shi, Kanagawa, Japan.
B&S Analytik, BioMedicalCenter, Dortmund, Germany.
PLoS One. 2014 Dec 9;9(12):e114555. doi: 10.1371/journal.pone.0114555. eCollection 2014.
Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique.
The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation.
A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%.
In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.
包括计算机断层扫描（CT）和支气管镜检查在内的传统肺癌检测方法昂贵且具有侵入性。因此，仍然需要一种最佳的肺癌检测技术。
对50例经支气管镜活检样本组织学证实为肺癌的患者（32例腺癌、10例鳞状细胞癌、8例小细胞癌）的呼出气体进行离子迁移谱（IMS）分析，并与39名健康志愿者进行比较。作为次要评估，我们比较了有和没有表皮生长因子受体（EGFR）突变的腺癌患者。
决策树算法可以区分包括腺癌、鳞状细胞癌和小细胞癌在内的肺癌患者。分析了115个分离出的挥发性有机化合物（VOC）峰。使用IMS数据库将峰-2标记为正十二烷，其能够以70.0%的灵敏度和89.7%的特异性区分数值。采用从正十二烷开始的决策树算法，灵敏度达到76%，特异性达到100%。比较腺癌患者和健康受试者之间的VOC峰，正十二烷能够以81.3%的灵敏度和89.7%的特异性区分数值。14例EGFR突变阳性患者的正十二烷含量显著高于14例EGFR阴性患者（p<0.01），灵敏度为85.7%，特异性为78.6%。
在这项前瞻性研究中，使用决策树算法的VOC峰模式对肺癌检测有用。此外，对腺癌患者进行正十二烷分析可能有助于鉴别EGFR突变。
