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Cellular Oncology
Volume 26, Issue 3, Pages 101-117

Quality Assurance System Using Statistical Process Control: An Implementation for Image Cytometry

David Chiu,1,6 Martial Guillaud,1 Dennis Cox,2 Michele Follen,3,4,5 and Calum MacAulay1

1Department of Cancer Imaging, British Columbia Cancer Agency, Vancouver, BC, Canada
2Department of Statistics, Rice University, Houston, TX, USA
3Department of Gynecology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
4Biomedical Engineering Center, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
5The Department of Gynecology, Obstetrics and Reproductive Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
6Perceptronix Medical Inc., Vancouver, BC, Canada

Received 13 October 2003; Accepted 4 March 2004

Copyright © 2004 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aims: Optical technologies have shown some promise for improving the care of cervical neoplasia. We are currently evaluating fluorescence and reflectance spectroscopy and quantitative cyto‐histopathology for cervical neoplasia screening and diagnosis. Here we describe the establishment and application of a quality assurance (QA) system for detecting system malfunctions and assessing the comparability of four image cytometers used in a multicenter clinical trial. Methods: Our QA system involves three levels of evaluation based on the periodicity and complexity of the measurements. We implemented our QA system at three image cytometers at the British Columbia Cancer Agency and one at M.D. Anderson Cancer Center. The measurements or tasks were performed daily, monthly, and semi‐annually. The current and voltage of the lamp, the calibration image characteristics, and the room temperature were checked daily. Long‐term stability over time, short‐term variability over time, and spatial response field uniformity were evaluated monthly. Camera linearity was measured semi‐annually. Control charts based on statistical process control techniques were used to detect when the system did not perform optimally. Results: Daily measurements have shown good consistency in room temperature, lamp and calibration behaviour. Monthly measurements have shown small coefficients of variation between and within the four devices. There have been greater differences between sessions than within sessions. Comparability among the four systems is reasonably good. Semi‐annual measurements have shown stable camera linearity. QA events were detected using the QA system. Multiple examples of event detection leading to correction of system malfunction are described in this report. Conclusions: QA programs are critical for ensuring data integrity and therefore for the conduct of multicenter clinical trials.