Evidence-Based Pathology: A Stable Set of Principles for a Rapidly Evolving Specialty

Chapter

Abstract

Modern technologies and ever more incisive methods of tissue analysis are providing increasing accuracy, resolution, and effectiveness to modern diagnostic sciences. We are immersed in a rapidly evolving world where disruptive technologies come at such speed, and information is generated in such abundance that evidence-based pathology (EBM) becomes an essential philosophical and practical factor of stability. It behooves all of us in pathology to establish evidence-based pathology as the linkage of technological innovation and research to the resolution of patient illness and problems in the delivery of care.

Keywords

Evidence-based pathology Diagnostic pathology Immunohistochemistry and evidence-based medicine Molecular medicine and evidence-based medicine Patient–physician relationship and evidence-based medicine 

References

  1. 1.
    Hardy A, Tansy EM. Medical enterprise and global response, 1945–2000. In: The Western medical tradition 1800–2000. Cambridge: Cambridge University Press; 2006.Google Scholar
  2. 2.
    Office of Technology Assessment. Assessing the efficacy and safety of medical technologies (OTA-H-75). Washington, DC: OTHA; 1978.Google Scholar
  3. 3.
    Wolff AC et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Arch Pathol Lab Med. 2007;131:18–43.PubMedGoogle Scholar
  4. 4.
    Costa J. Systems approach to the practice of pathology: a new role for the pathologist. Arch Pathol Lab Med. 2009;133:524–6.PubMedGoogle Scholar
  5. 5.
    Hayden EC. Personalized cancer therapy gets closer. Nature. 2009;458:131–2.PubMedCrossRefGoogle Scholar
  6. 6.
    Brown RE. Morphoproteomics: exposing protein circuitries in tumors to identify potential therapeutic targets in cancer patients. Expert Rev Proteomics. 2005;2:337–48.PubMedCrossRefGoogle Scholar
  7. 7.
    Lievre A, Blons H, Laurent-Puig P. Oncogenic ­mutations as predictive factors in colorectal cancer. Oncogene. 2010;29:3033–43.PubMedCrossRefGoogle Scholar
  8. 8.
    Mousses S et al. Using biointelligence to search the cancer genome: an epistemological perspective on knowledge recovery strategies to enable precision medical genomics. Oncogene. 2008;Suppl 2:S-58–66.Google Scholar
  9. 9.
    Choi M et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci USA. 2009;106:19096–101.PubMedCrossRefGoogle Scholar
  10. 10.
    Donovan MJ et al. Personalized prediction of tumor response and cancer progression on prostate needle biopsy. J Urol. 2009;182:125–32.PubMedCrossRefGoogle Scholar
  11. 11.
    Donovan MJ et al. A systems pathology model for predicting overall survival in patients with refractory, advanced non-small-cell lung cancer treated with gefitinib. Eur J Cancer. 2009;45:1518–26.PubMedCrossRefGoogle Scholar
  12. 12.
    Topol EJ. Transforming medicine via digital innovation. Sci Transl Med. 2010;2:16.Google Scholar
  13. 13.
    Liu J et al. Complexity of coupled human and natural systems. Science. 2007;317:1513–6.PubMedCrossRefGoogle Scholar
  14. 14.
    Rabinow P. Artificiality and enlightenment. From sociobiology to biosociality. In: Essays on the anthropology of reason. Princeton: Princeton University Press; 1996.Google Scholar
  15. 15.
    Straus SE, McAlister FA. Evidence-based medicine: a commentary on common criticisms. CMAJ. 2000;163:837–41.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of PathologyYale School of MedicineNew HavenUSA

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