The use of a gas chromatography-sensor system combined with advanced statistical methods, towards the diagnosis of urological malignancies

Abstract

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.

Figure 1

Pipeline for data analysis.

Figure 2

Chromatograms showing baseline correction using a 2 step procedure

Figure 3

Schematic for selecting the reference sample for chromatogram alignment.

Figure 4

Validation schemes used to validate the classifiers built by the pipeline presented here: (a) repeated 10-fold cross-validation; (b) repeated double cross-validation; (c) Repeated 10-fold cross-validation Monte Carlo; and (d) repeated double cross-validation Monte Carlo.

Figure 5

Boxplot (a) and two-factor principal component analysis (b) of data derived from patients with prostate cancer and controls

Figure 6

ROC curves of repeated double cross-validation applied to Prostate and Control urine sample data processed by the GC-sensor pipeline and modelled by linear discriminant analysis (LDA) and support vector machine with polynomial kernel (SVM-P).

Figure 7

Sensitivity and specificity data of biomarkers for prostate cancer. Grey rows = biomarkers found in the literature; Orange rows = GC-sensor pipeline; VOC = volatile organic compound; Validation = the statistical method used to validate sample classification; LOOCV = leave-one-out cross-validation; 10-FoldCV = 30 times repeated 10-fold cross-validation; DoubleCV = 30 time repeated double cross-validation (outer loop = 3 folds; inner loop = 10 fold cross-validation repeated 5 times); PSA = prostate-specific antigen; p2PSA = serum isoform [-2] proPSA; PHI = prostate healthy index; imPSA = intracellular macrophage PSA; PCA3 = prostate cancer antigen 3; Not validated = no statistical method was applied to validate results of sample classification.

Figure 8

Boxplot (a) and two-factor principal component analysis (b) of data derived from patients with bladder cancer and controls

Figure 9

ROC curves of repeated double cross-validation applied to Bladder and Control urine sample data processed by the GC-sensor pipeline and modelled by linear discriminant analysis (LDA) and support vector machine with polynomial kernel (SVM-P).

Figure 10

Sensitivity and specificity data of biomarkers for bladder cancer. Grey rows = biomarkers found in the literature; Orange rows = framework presented here; BTA = bladder tumor antigen; NMP-22 = nuclear matrix protein 22; Hb dipstick = hemoglobin dipstick; Cxbladder = Cxbladder Detect; VOC = volatile organic compound; Validation = the statistical method used to validate sample classification; LOOCV = leave-one-out cross-validation; 10-FoldCV= 30 times repeated 10-fold cross-validation; DoubleCV = 30 time repeats of 3-fold double cross-validation with an inner loop of 10-fold cross-validation repeated 5 times; Not specified = a statistical validation method for sample classification was not found in source.