Aggio Raphael B M, de Lacy Costello Ben, White Paul, Khalid Tanzeela, Ratcliffe Norman M, Persad Raj, Probert Chris S J
Institute of Translational Medicine, Department of Cellular and Molecular Physiology, University of Liverpool, Liverpool, UK.
J Breath Res. 2016 Feb 11;10(1):017106. doi: 10.1088/1752-7155/10/1/017106.
Prostate cancer is one of the most common cancers. Serum prostate-specific antigen (PSA) is used to aid the selection of men undergoing biopsies. Its use remains controversial. We propose a GC-sensor algorithm system for classifying urine samples from patients with urological symptoms. This pilot study includes 155 men presenting to urology clinics, 58 were diagnosed with prostate cancer, 24 with bladder cancer and 73 with haematuria and or poor stream, without cancer. Principal component analysis (PCA) was applied to assess the discrimination achieved, while linear discriminant analysis (LDA) and support vector machine (SVM) were used as statistical models for sample classification. Leave-one-out cross-validation (LOOCV), repeated 10-fold cross-validation (10FoldCV), repeated double cross-validation (DoubleCV) and Monte Carlo permutations were applied to assess performance. Significant separation was found between prostate cancer and control samples, bladder cancer and controls and between bladder and prostate cancer samples. For prostate cancer diagnosis, the GC/SVM system classified samples with 95% sensitivity and 96% specificity after LOOCV. For bladder cancer diagnosis, the SVM reported 96% sensitivity and 100% specificity after LOOCV, while the DoubleCV reported 87% sensitivity and 99% specificity, with SVM showing 78% and 98% sensitivity between prostate and bladder cancer samples. Evaluation of the results of the Monte Carlo permutation of class labels obtained chance-like accuracy values around 50% suggesting the observed results for bladder cancer and prostate cancer detection are not due to over fitting. The results of the pilot study presented here indicate that the GC system is able to successfully identify patterns that allow classification of urine samples from patients with urological cancers. An accurate diagnosis based on urine samples would reduce the number of negative prostate biopsies performed, and the frequency of surveillance cystoscopy for bladder cancer patients. Larger cohort studies are planned to investigate the potential of this system. Future work may lead to non-invasive breath analyses for diagnosing urological conditions.
前列腺癌是最常见的癌症之一。血清前列腺特异性抗原（PSA）用于辅助选择接受活检的男性。其使用仍存在争议。我们提出了一种气相色谱传感器算法系统，用于对有泌尿系统症状患者的尿液样本进行分类。这项初步研究纳入了155名到泌尿外科门诊就诊的男性，其中58人被诊断为前列腺癌，24人患有膀胱癌，73人有血尿和/或排尿不畅但无癌症。应用主成分分析（PCA）来评估所实现的区分度，同时使用线性判别分析（LDA）和支持向量机（SVM）作为样本分类的统计模型。采用留一法交叉验证（LOOCV）、重复十折交叉验证（10FoldCV）、重复双交叉验证（DoubleCV）和蒙特卡罗置换来评估性能。发现前列腺癌与对照样本、膀胱癌与对照样本以及膀胱癌与前列腺癌样本之间存在显著分离。对于前列腺癌诊断，气相色谱/支持向量机系统在留一法交叉验证后对样本分类的灵敏度为95%，特异性为96%。对于膀胱癌诊断，支持向量机在留一法交叉验证后报告的灵敏度为96%，特异性为100%，而双交叉验证报告的灵敏度为87%，特异性为99%，支持向量机在前列腺癌和膀胱癌样本之间显示的灵敏度分别为78%和98%。对类标签的蒙特卡罗置换结果评估获得了约50%的类似随机的准确率值，这表明观察到的膀胱癌和前列腺癌检测结果并非由于过度拟合。此处呈现的初步研究结果表明，气相色谱系统能够成功识别出可对泌尿系统癌症患者的尿液样本进行分类的模式。基于尿液样本的准确诊断将减少阴性前列腺活检的数量以及膀胱癌患者的监测膀胱镜检查频率。计划开展更大规模的队列研究来探究该系统的潜力。未来的工作可能会带来用于诊断泌尿系统疾病的非侵入性呼吸分析。
