Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer.
Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer.
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使用比色法传感器阵列进行呼气呼吸分析，以鉴定和表征肺癌。
DOI：
10.1097/jto.0b013e318233d80f
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发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
Sasidhar M
中科院分区：
文献类型：
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作者：
Mazzone PJ;Wang XF;Xu Y;Mekhail T;Beukemann MC;Na J;Kemling JW;Suslick KS;Sasidhar M
呼出的挥发性有机化合物的模式代表了一种代谢生物特征，具有识别和表征肺癌的潜力。基于呼吸生物信号的肺癌同质亚组分类可能比全球呼吸信号更准确。将呼吸生物特征与临床风险因素结合起来可能会提高特征的准确性。使用比色传感器阵列开发肺癌的呼气生物特征。确定是否包含临床危险因素的肺癌特征的呼吸生物特征的准确性。229名研究对象，92名肺癌患者和137名对照，呼出的呼吸被绘制成一个比色传感器阵列。根据传感器的颜色变化，建立了Logistic预测模型，并进行了统计验证。年龄、性别、吸烟史和COPD被纳入预测模型。经过验证的呼吸和临床生物特征相结合的预测模型在区分肺癌和对照受试者方面是适度准确的(C-统计0.811)。当模型只关注一种组织学时，准确度提高了(C-统计量0.825-0.890)。具有不同组织结构的个体可以准确地相互区分(C-统计量0.889，腺癌与鳞癌)。对于分期和存活期的确认呼吸生物特征，注意到中等的准确性(C-统计量分别为0.793和0.770)。一种比色传感器阵列能够识别肺癌的呼气生物特征。呼吸生物特征的准确性可以通过评估特定的组织结构和纳入临床风险因素来优化。
The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature. Develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array. Determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and COPD were incorporated in the prediction models. The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825 – 0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.889 for adenocarcinoma vs. squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.793, 0.770 respectively). A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.