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Evaluation of gas chromatography mass spectrometry and pattern recognition for the identification of bladder cancer from urine headspace

机译:气相色谱质谱法和模式识别技术在尿液顶空鉴别膀胱癌中的应用

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摘要

Previous studies have indicated that volatile organic compounds specific to bladder cancer may exist in urine headspace, raising the possibility that they may be of diagnostic value for this particular cancer. To further examine this hypothesis, urine samples were collected from patients diagnosed with either bladder cancer or a non-cancerous urological disease/infection, and from healthy volunteers, from which the volatile metabolomes were analysed using gas chromatography mass spectrometry. The acquired data were subjected to a specifically designed pattern recognition algorithm, involving cross-model validation. The best diagnostic performance, achieved with independent test data provided by healthy volunteers and bladder cancer patients, was 89% overall accuracy (90% sensitivity and 88% specificity). Permutation tests showed that these were statistically significant, providing further evidence of the potential for volatile biomarkers to form the basis of a non-invasive diagnostic technique.
机译:先前的研究表明,尿液顶空可能存在特定于膀胱癌的挥发性有机化合物,这增加了它们可能对该特定癌症具有诊断价值的可能性。为了进一步检验这一假设,从被诊断患有膀胱癌或非癌性泌尿系统疾病/感染的患者以及健康志愿者收集尿液样本,然后使用气相色谱质谱法分析其中的挥发性代谢物。采集的数据经过专门设计的模式识别算法,涉及跨模型验证。使用健康志愿者和膀胱癌患者提供的独立测试数据可获得的最佳诊断性能是89%的整体准确度(90%的敏感性和88%的特异性)。排列测试显示,这些具有统计学意义,为挥发性生物标记物形成无创诊断技术的基础提供了进一步的证据。

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