Errors in Radiology: A Biostatistical Framework

  • Francesco Sardanelli
  • Giovanni Di Leo


An error in radiology is an event commonly thought of in association with a malpractice claim by a patient. However, this is only a relatively weak association. Not all errors can be judged as malpractice. The majority of errors do not determine harm to patients, while a minority of them may cause relevant harm, including fatal consequences. For example, for emergency computed tomography (CT), the discrepancy rate between the initial report and the secondary interpretation is 6–27% but a change in patient management occurs in only 1–5% of these cases; the corresponding values for review of cross-sectional imaging in oncology are 12–19% and 3–9%, respectively [1]. Errors are part of our human and professional life. We expect errors, even though we try to keep the error rate as low as possible. To deal with errors is a matter of numbers and statistics, beginning with the calculation of an error rate. Error analysis is of paramount relevance, both for the learning process and for strategies aimed at reducing the error rate [2]. This is the rationale for attempts to outline a biostatistical framework for errors in radiology.


Diagnostic Performance Interobserver Variability Screen Mammography Malpractice Claim Radiological Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    FitzGerald R (2005) Radiological error: analysis, standard setting, targeted instruction and teamworking. Eur Radiol 15:1760–1767PubMedCrossRefGoogle Scholar
  2. 2.
    Pinto A, Acampora C, Pinto F et al (2011) Learning from diagnostic errors: a good way to improve education in radiology. Eur J Radiol 78:372–376PubMedCrossRefGoogle Scholar
  3. 3.
    Nottingham J, Anderson M (1999) Errors in radiology and pathology. Lancet 354:1560PubMedCrossRefGoogle Scholar
  4. 4.
    Plebani M (2010) The detection and prevention of errors in laboratory medicine. Ann Clin Biochem 47:101–110PubMedCrossRefGoogle Scholar
  5. 5.
    Sardanelli F, Di Leo G (2009). Biostatistics for radiologists. Springer-Verlag, Milan, pp 125 (a), pp 32-40 (b), pp 125-140 (c), 165-179 (d)CrossRefGoogle Scholar
  6. 6.
    Di Leo G (2011) Challenges in estimating reproducibility of imaging modalities. World J Methodol 1:12–14CrossRefGoogle Scholar
  7. 7.
    Sardanelli F, Hunink MG, Gilbert FJ et al (2010) Evidence-based radiology: why and how? Eur Radiol 20:1–15PubMedCrossRefGoogle Scholar
  8. 8.
    Pinto A, Brunese L (2010) Spectrum of diagnostic errors in radiology. World J Radiol 2:377-3839. In American College of Radiology (ACR) (2003). Breast Imaging Reporting and Data System (BI-RADS) Atlas. 4th ed. Reston, VAGoogle Scholar
  9. 9.
    Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  10. 10.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310PubMedCrossRefGoogle Scholar
  11. 11.
    Sardanelli F, Quarenghi M, Di Leo G et al (2008) Segmentation of cardiac cine MR images of left and right ventricles: interactive semiautomated methods and manual contouring by two readers with different education and experience. J Magn Reson Imaging 27:785–792PubMedCrossRefGoogle Scholar
  12. 12.
    Sardanelli F, Esseridou A, Del Sole 6A, Sconfienza LM (2012) Response to treatment: The role of imaging. In: Aglietta M, Regge D (eds) Imaging tumor response to treatment. Springer-Verlag, MilanGoogle Scholar
  13. 13.
    Jones RH, Velazquez EJ, Michler RE, et al; STICH Hypothesis 2 Investigators (2009) Coronary bypass surgery with or without surgical ventricular reconstruction. N Engl J Med 360:1705–1717Google Scholar
  14. 14.
    Hans DB, Shepherd JA, Schwartz EN et al (2008) Peripheral dual-energy X-ray absorptiometry in the management of osteoporosis: the 2007 ISCD Official Positions. J Clin Densitom 11:188–206PubMedCrossRefGoogle Scholar
  15. 15.
    Sardanelli F (2012) Evidence-based radiology and its relation to quality. In: Abujudeh HH, Bruno MA (eds) Quality and safety in radiology. Oxford University Press, New York, Chapter 27Google Scholar
  16. 16.
    Kelly S, Berry E, Roderick P, et al (1997) The identification of bias in studies of the diagnostic performance of imaging modalities. Br J Radiol 70:1028–1035PubMedGoogle Scholar
  17. 17.
    Sica GT (2006) Bias in research studies. Radiology 238:780–789PubMedCrossRefGoogle Scholar
  18. 18.
    Reid MC, Lachs MS, Feinstein AR (1995) Use of methodological standards in diagnostic test research. Getting better but still not good. JAMA 274:645–651PubMedCrossRefGoogle Scholar
  19. 19.
    Bossuyt PM, Reitsma JB, Bruns DE et al (2003) Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. Radiology 226:24–28PubMedCrossRefGoogle Scholar
  20. 20.
    Smidt N, Rutjes AW, van der Windt DA et al (2005) Quality of reporting of diagnostic accuracy studies. Radiology 235:347–353PubMedCrossRefGoogle Scholar
  21. 21.
    Wilczynski NL (2008) Quality of reporting of diagnostic accuracy studies: no change since STARD statement publication—before-and-after study. Radiology 248:817–823PubMedCrossRefGoogle Scholar
  22. 22.
    Dodd JD, MacEneaney PM, Malone DE (2004) Evidence-based radiology: how to quickly assess the validity and strength of publications in the diagnostic radiology literature. Eur Radiol 14:915–922PubMedCrossRefGoogle Scholar
  23. 23.
    Whiting P, Rutjes AW, Reitsma JB et al (2003) The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 3:25PubMedCrossRefGoogle Scholar
  24. 24.
    Whiting PF, Weswood ME, Rutjes AW et al (2006) Evaluation of QUADAS, a tool for the quality assessment of diagnostic accuracy studies. BMC Med Res Methodol 6:9PubMedCrossRefGoogle Scholar
  25. 25.
    Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536PubMedGoogle Scholar
  26. 26.
    Devlin K (2008) The unfinished game: Pascal, Fermat, and the seventeenth-century letter that made the world modern. Basic Books, New York, NYGoogle Scholar

Copyright information

© Springer-Verlag Italia 2012

Authors and Affiliations

  • Francesco Sardanelli
    • 1
    • 2
  • Giovanni Di Leo
    • 1
    • 2
  1. 1.Department of Medical and Surgical SciencesUniversity of MilanItaly
  2. 2.Radiology Unit“San Donato” Hospital IRCCSSan Donato Milanese (MI)Italy

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