The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

F. Abogunrin, Hugh O'Kane, M.W. Ruddock, Michael Stevenson, C.N. Reid, Joe O'Sullivan, N.H. Anderson, D. O'Rourke, B. Duggan, J.V. Lamont, Peter Hamilton, T. Nambirajan, Kathleen Williamson

Research output: Contribution to journalArticle

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Abstract

BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P
Original languageEnglish
Pages (from-to)2641-2650
Number of pages10
JournalCancer
Volume118
Issue number10
DOIs
Publication statusPublished - 15 May 2011

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Hematuria
Urinary Bladder Neoplasms
Biomarkers
Demography
Neoplasms
Carcinoembryonic Antigen
Area Under Curve
Case-Control Studies
Logistic Models
Enzyme-Linked Immunosorbent Assay
Regression Analysis
Urine
Pathology
Technology
Serum

Cite this

Abogunrin, F., O'Kane, H., Ruddock, M. W., Stevenson, M., Reid, C. N., O'Sullivan, J., ... Williamson, K. (2011). The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria. Cancer, 118(10), 2641-2650. https://doi.org/10.1002/cncr.26544
Abogunrin, F. ; O'Kane, Hugh ; Ruddock, M.W. ; Stevenson, Michael ; Reid, C.N. ; O'Sullivan, Joe ; Anderson, N.H. ; O'Rourke, D. ; Duggan, B. ; Lamont, J.V. ; Hamilton, Peter ; Nambirajan, T. ; Williamson, Kathleen. / The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria. In: Cancer. 2011 ; Vol. 118, No. 10. pp. 2641-2650.
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Abogunrin, F, O'Kane, H, Ruddock, MW, Stevenson, M, Reid, CN, O'Sullivan, J, Anderson, NH, O'Rourke, D, Duggan, B, Lamont, JV, Hamilton, P, Nambirajan, T & Williamson, K 2011, 'The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria', Cancer, vol. 118, no. 10, pp. 2641-2650. https://doi.org/10.1002/cncr.26544

The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria. / Abogunrin, F.; O'Kane, Hugh; Ruddock, M.W.; Stevenson, Michael; Reid, C.N.; O'Sullivan, Joe; Anderson, N.H.; O'Rourke, D.; Duggan, B.; Lamont, J.V.; Hamilton, Peter; Nambirajan, T.; Williamson, Kathleen.

In: Cancer, Vol. 118, No. 10, 15.05.2011, p. 2641-2650.

Research output: Contribution to journalArticle

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T1 - The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

AU - Abogunrin, F.

AU - O'Kane, Hugh

AU - Ruddock, M.W.

AU - Stevenson, Michael

AU - Reid, C.N.

AU - O'Sullivan, Joe

AU - Anderson, N.H.

AU - O'Rourke, D.

AU - Duggan, B.

AU - Lamont, J.V.

AU - Hamilton, Peter

AU - Nambirajan, T.

AU - Williamson, Kathleen

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AB - BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P

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