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A Pilot Study Combining a GC-Sensor Device with a Statistical Model for the Identification of Bladder Cancer from Urine Headspace

Ewen, Richard; Probert, Chris S.; Ratcliffe, Norman; Khalid, Tanzeela; White, Paul; Ewen, R.J.; De Lacy Costello, Ben; Persad, Raj; Probert, C.S.J.; Ratcliffe, Norman M.; Johnson, Emmanuel

A Pilot Study Combining a GC-Sensor Device with a Statistical Model for the Identification of Bladder Cancer from Urine Headspace Thumbnail


Authors

Richard Ewen

Chris S. Probert

Norman Ratcliffe

Tanzeela Khalid

Paul White Paul.White@uwe.ac.uk
Professor in Applied Statistics

R.J. Ewen

Raj Persad

C.S.J. Probert

Norman Ratcliffe Norman.Ratcliffe@uwe.ac.uk
Professor in Materials & Sensors Science

Emmanuel Johnson



Abstract

There is a need to reduce the number of cystoscopies on patients with haematuria. Presently there are no reliable biomarkers to screen for bladder cancer. In this paper, we evaluate a new simple in-house fabricated, GC-sensor device in the diagnosis of bladder cancer based on volatiles. Sensor outputs from 98 urine samples were used to build and test diagnostic models. Samples were taken from 24 patients with transitional (urothelial) cell carcinoma (age 27-91 years, median 71 years) and 74 controls presenting with urological symptoms, but without a urological malignancy (age 29-86 years, median 64 years); results were analysed using two statistical approaches to assess the robustness of the methodology. A two-group linear discriminant analysis method using a total of 9 time points (which equates to 9 biomarkers) correctly assigned 24/24 (100%) of cancer cases and 70/74 (94.6%) controls. Under leave-one-out cross-validation 23/24 (95.8%) of cancer cases were correctly predicted with 69/74 (93.2%) of controls. For partial least squares discriminant analysis, the correct leave-one-out cross-validation prediction values were 95.8% (cancer cases) and 94.6% (controls). These data are an improvement on those reported by other groups studying headspace gases and also superior to current clinical techniques. This new device shows potential for the diagnosis of bladder cancer, but the data must be reproduced in a larger study. © 2013 Khalid et al.

Journal Article Type Article
Publication Date Jul 8, 2013
Deposit Date Jul 11, 2013
Publicly Available Date Apr 4, 2016
Journal PLoS ONE
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 8
Issue 7
DOI https://doi.org/10.1371/journal.pone.0069602
Keywords GC-Sensor device, statistical model, bladder cancer, urine headspace
Public URL https://uwe-repository.worktribe.com/output/929877
Publisher URL http://dx.doi.org/10.1371/journal.pone.0069602
Contract Date Apr 4, 2016

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