Mazzone Peter J, Wang Xiao-Feng, Lim Sung, Jett James, Choi Humberto, Zhang Qi, Beukemann Mary, Seeley Meredith, Martino Ray, Rhodes Paul
1 Respiratory Institute, Cleveland Clinic, Cleveland, Ohio.
Ann Am Thorac Soc. 2015 May;12(5):752-7. doi: 10.1513/AnnalsATS.201411-540OC.
Volatile organic compounds present in the exhaled breath have shown promise as biomarkers of lung cancer. Advances in colorimetric sensor array technology, breath collection methods, and clinical phenotyping may lead to the development of a more accurate breath biomarker.
Perform a discovery-level assessment of the accuracy of a colorimetric sensor array-based volatile breath biomarker.
Subjects with biopsy-confirmed untreated lung cancer, and others at risk for developing lung cancer, performed tidal breathing into a breath collection instrument designed to expose a colorimetric sensor array to the alveolar portion of the breath. Random forest models were built from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and subgroups from control, and to characterize the cancer. Additional models were developed after matching the clinical phenotypes of cancer and control subjects.
Ninety-seven subjects with lung cancer and 182 control subjects participated. The accuracies, reported as C-statistics, for models of cancer and subgroups versus control ranged from 0.794 to 0.861. The accuracy was improved by developing models for cancer and control groups selected through propensity matching for clinical variables. A model built using only subjects from the largest available clinical subgroup (49 subjects) had a C-statistic of 0.982. Models developed and tested to characterize cancer histology, and to compare early- with late-stage cancer, had C-statistics of 0.881-0.960.
The colorimetric sensor array signature of exhaled breath volatile organic compounds was capable of distinguishing patients with lung cancer from clinically relevant control subjects in a discovery level trial. The incorporation of clinical phenotypes into the further development of this biomarker may optimize its accuracy.
呼出气体中存在的挥发性有机化合物已显示出作为肺癌生物标志物的潜力。比色传感器阵列技术、呼气收集方法和临床表型分析的进展可能会导致开发出更准确的呼气生物标志物。
对比色传感器阵列基挥发性呼气生物标志物的准确性进行探索性评估。
经活检确诊为未经治疗的肺癌患者以及其他有患肺癌风险的受试者,通过潮式呼吸向一种呼气收集仪器中呼气,该仪器旨在使比色传感器阵列暴露于呼气的肺泡部分。随机森林模型基于70%的研究对象的传感器输出构建,并针对其余30%的对象进行测试。构建模型以区分癌症患者和亚组与对照组,并对癌症进行特征描述。在匹配癌症患者和对照对象的临床表型后,又开发了其他模型。
97名肺癌患者和182名对照对象参与了研究。癌症和亚组与对照组模型的准确性(以C统计量表示)范围为0.794至0.861。通过为通过临床变量倾向匹配选择的癌症和对照组开发模型,准确性得到了提高。仅使用最大可用临床亚组(49名对象)中的对象构建的模型的C统计量为0.982。为表征癌症组织学以及比较早期和晚期癌症而开发和测试的模型的C统计量为0.881至0.960。
在一项探索性试验中,呼出气体挥发性有机化合物的比色传感器阵列特征能够将肺癌患者与临床相关对照对象区分开来。将临床表型纳入该生物标志物的进一步开发中可能会优化其准确性。