Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer.
Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer.
复制标题
挥发性尿液生物标志物检测和表征肺癌的准确性。
DOI：
10.1186/s12885-015-1996-0
复制
发表时间：
2015-12-23
期刊：
影响因子：
3.8
通讯作者：
Rhodes P
中科院分区：
文献类型：
--
作者：
Mazzone PJ;Wang XF;Lim S;Choi H;Jett J;Vachani A;Zhang Q;Beukemann M;Seeley M;Martino R;Rhodes P
尿液顶空气体中挥发性有机化合物的混合物可能能够区分肺癌患者和相关对照人群。活检证实未治疗肺癌的受试者和其他有发生肺癌风险的受试者提供了尿液样本。将比色传感器阵列暴露于纯的和预处理的尿样的顶部空间气体。随机森林模型是从70%的研究对象的传感器输出中训练出来的，并针对剩余的30%进行测试。开发模型以将癌症和癌症亚组与对照分开，并表征癌症。在最大的临床亚组上开发了一个额外的模型。90名肺癌受试者和55名对照受试者参与了研究。癌症或癌症亚组与对照模型的准确度（报告为C统计量）范围为0.795 - 0.917。仅使用来自最大可用临床亚组（30例受试者）的受试者构建的肺癌与对照模型的C统计量为0.970。为表征癌症组织学特征和比较早期与晚期癌症而开发和测试的模型的C统计量分别为0.849和0.922。尿液顶部空间中挥发性有机化合物的比色传感器阵列特征可能能够将肺癌患者与临床相关对照区分开。将临床表型纳入该生物标志物的开发中可以优化其准确性。
The mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations. Subjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70 % of the study subjects and were tested against the remaining 30 %. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup. 90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 – 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively. The colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy.