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22 - Performance Assessment Using Standardized Data Sets: The PERFORMS Scheme in Breast Screening and Other Domains

from Part IV - Clinical Performance Assessment

Published online by Cambridge University Press:  20 December 2018

Ehsan Samei
Affiliation:
Duke University Medical Center, Durham
Elizabeth A. Krupinski
Affiliation:
Emory University, Atlanta
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Publisher: Cambridge University Press
Print publication year: 2018

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References

Boyd, N.F., Rommens, J.M., Vogt, K., Lee, V., Hopper, J.L., Yaffe, M.J., Paterson, A.D. (2005). Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol, 6, 798780.Google Scholar
Burns, F.G. (2011). An Independent External Review of the Breast Screening Unit at East Lancashire NHS Trust. Burnley, UK: East Lancashire Hospitals NHS Trust.Google Scholar
Chen, Y. (2010). Intelligent computing applications based on eye gaze: their role in mammographic interpretation training. PhD thesis, Loughborough University, UK.Google Scholar
Chen, Y., Gale, A.G., Evanoff, M. (2013). Does routine breast screening practice over-ride display quality in reporting enriched test sets? Proc SPIE Med Imag, 8673, 86730v.Google Scholar
Chen, Y., James, J.J., Turnbull, A.E., Gale, A.G. (2015). The use of lower resolution viewing devices for mammographic interpretation: implications for education and training. Eur J Radiol, 25, 30033008.Google Scholar
Chen, Y., Dong, L., Nevisi, H., Gale, A.G. (2016). The international use of PERFORMS mammographic test sets. In: Tingberg, A., Lång, K., Timberg, P. (eds.) Breast Imaging: 13th International Workshop, IWDM 2016, Lecture Notes in Computer Science. Geneva, Switzerland: Springer, pp. 130135.Google Scholar
Chen, Y., James, J., Dong, L., Gale, A.G. (2017). Measuring performance in the interpretation of chest radiographs – a pilot study. Clin Radiol, 72, 230235.Google Scholar
Cowley, H.C., Gale, A.G. (1997). Time of day effects on mammographic film reading performance. Proc SPIE Med Imag, 3036, 212–221.Google Scholar
Cowley, H.C., Gale, A.G. (1999). Breast cancer screening: comparison of radiologists’ performance in a self-assessment scheme and in actual breast screening. Proc SPIE Med Imag, 3663, 157168.Google Scholar
Darker, I.T., Chen, Y., Gale, A.G. (2011). Health professionals’ agreement on density judgements and successful abnormality identification within the UK breast screening programme. Proc SPIE Med Imag, 7966, 796604.CrossRefGoogle Scholar
Dong, L., Chen, Y., Gale, A.G., Chakraborty, D.P. (2012). A potential method to identify poor breast screening performance? Proc SPIE Med Imag, 8318, 831819.Google Scholar
Esserman, L., Cowley, H., Eberle, C., Kirkpatrick, A., Chang, S., Berbaum, K., Gale, A.G. (2002). Improving the accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst, 94, 369375.CrossRefGoogle ScholarPubMed
Findlay, J.M., Gilchrist, I.D. (2003). Active Vision: The Psychology of Looking and Seeing. Oxford, England: Oxford University Press.Google Scholar
Forrest, A.P.M. (1986). Report to the Health Ministers of England, Wales, Scotland and Northern Ireland. London: HMSO.Google Scholar
Gale, A.G. (1997). Human response to visual stimuli. In: Hendee, W., Wells, P. (eds.) Perception of Visual Information. New York, NY: Springer Verlag, pp. 127–147.Google Scholar
Gale, A.G. (2003). PERFORMS – a self-assessment scheme for radiologists in breast screening. Semin Breast Dis, 6, 148152.Google Scholar
Gale, A.G., Walker, G.E. (1991). Design for performance: quality assessment in a national breast screening programme. In: Lovesay, E. (ed.) Ergonomics – Design for Performance 1991. London, England: Taylor & Francis.Google Scholar
Gale, A.G., Roebuck, E.J., Riley, P., Worthington, B.S. (1987). Computer aids to mammography diagnosis. Br J Radiol, 60, 887891.Google Scholar
Garland, L.H. (1949). On the scientific evaluation of diagnostic procedures. Radiology, 52, 309328.Google Scholar
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137144.Google Scholar
Krupinski, E.A., Berbaum, K.S. (2010). Does reader visual fatigue impact interpretation accuracy? Proc SPIE Med Imag, 7627, 762701.Google Scholar
Kundel, H.L., Nodine, C.F., Carmody, D. (1978). Visual scanning, pattern recognition and decision making in pulmonary nodule detection. Invest Radiol, 13, 175181.CrossRefGoogle ScholarPubMed
National Health Service. (2017). https://digital.nhs.uk/catalogue/PUB23376 (accessed November 2, 2017).Google Scholar
Neisser, U. (1976). Cognition and Reality. San Francisco, CA: W.H. Freeman.Google Scholar
Nevisi, H., Dong, L., Chen, Y., Gale, A.G. (2017). How quickly do breast screeners learn their skills? Proc SPIE Med Imag, 10136, 101360D.Google Scholar
Royal College of Radiologists. (1990). Quality Assurance Guidelines for Radiologists. London, UK: Royal College of Radiologists.Google Scholar
Scott, H.J., Gale, A.G (2005). Breast screening: when is a difficult case truly difficult and for whom? Proc SPIE Med Imag, 5749, 557565.Google Scholar
Scott, H.J., Gale, A.G., Wooding, D.S. (2004). Breast screening technologists: does real-life case volume affect performance? Proc SPIE Med Imag, 5372, 399406.Google Scholar
Scott, H.J., Evans, A., Gale, A.G., Murphy, A., Reed, J. (2009). The relationship between real life breast screening and an annual self-assessment scheme. Proc SPIE Med Imag, 7263, E1–E9.Google Scholar
Sickles, E.A., D’Orsi, C.J., Bassett, L.W., et al. (2013). ACR BI-RADS mammography. In: ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology.Google Scholar
Tang, Q., Dong, L., Chen, Y., Gale, A.G. (2017). The implementation of an AR (augmented reality) approach to support mammographic interpretation training – an initial feasibility study. Proc SPIE Med Imag, 10136, 1013604.Google Scholar
Yerushalmy, J. (1969). The statistical assessment of the variability in observer perception and description of roentgenographic pulmonary shadows. Radiol Clin N Am, 1, 381390.Google Scholar

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