Skip to main content

Introduction to Decision Tools

  • Chapter
  • First Online:
Decision Tools for Radiation Oncology

Part of the book series: Medical Radiology ((Med Radiol Radiat Oncol))

  • 1425 Accesses

Abstract

Decision tools are becoming critical to medical decision-making, due to the complexity of available information that outstrips the capacity to synthesize it without assistance. Tools such as decision trees, algorithms and nomograms may be used to facilitate treatment decisions, evaluating endpoints such as survival and toxicity. Incorporation of biologic and molecular data is increasingly being used in decision-making in cancer, for example in selecting systemic therapies, and will soon be expanded to include decisions about radiotherapy and surgery. Health technology is an integral part of decision tools development, allowing rapid access to data and distributed access to users. However, the limitations in the development and use of decision tools must be recognized, and solutions developed to facilitate their widespread implementation and improve healthcare outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alper BS, Hand JA, Elliott SG et al (2004) How much effort is needed to keep up with the literature relevant for primary care? J Med Libr Assoc 92:429–437

    PubMed Central  PubMed  Google Scholar 

  • Bast RC Jr, Ravdin P, Hayes DF et al (2001) 2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol 19:1865–1878

    PubMed  Google Scholar 

  • Cronin M, Sangli C, Liu ML et al (2007) Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem 53:1084–1091

    Article  CAS  PubMed  Google Scholar 

  • Davenport TH, Glaser J (2002) Just-in-time delivery comes to knowledge management. Harv Bus Rev 80:107–11

    Google Scholar 

  • Gage BF, Waterman AD, Shannon W et al (2001) Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA 285:2864–2870

    Article  CAS  PubMed  Google Scholar 

  • Gage BF, van Walraven C, Pearce L et al (2004) Selecting patients with atrial fibrillation for anticoagulation: stroke risk stratification in patients taking aspirin. Circulation 110:2287–2292

    Article  CAS  PubMed  Google Scholar 

  • Heemsbergen WD, Peeters ST, Koper PC et al (2006) Acute and late gastrointestinal toxicity after radiotherapy in prostate cancer patients: consequential late damage. Int J Radiat Oncol Biol Phys 66:3–10

    Article  PubMed  Google Scholar 

  • Hunink M, Glasziou P (2011) Decision making in health and medicine. Cambridge University Press, New York

    Google Scholar 

  • Italiano A (2011) Prognostic or predictive? It’s time to get back to definitions! J Clin Oncol 29:4718 (author reply 4718–9)

    Google Scholar 

  • Kanes AC, Healey JS, Cairns JA et al (2012) Focused 2012 update of the Canadian Cardiovascular Society atrial fibrillation guidelines: recommendations for stroke prevention and rate/rhythm control. Can J Cardiol 28:125–136

    Article  Google Scholar 

  • Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481

    Google Scholar 

  • Kattan MW (2007) Outcome prediction in cancer. Elsevier, Oxford

    Google Scholar 

  • Lau SK, Boutros PC, Pintilie M et al (2007) Three-gene prognostic classifier for early-stage non small-cell lung cancer. J Clin Oncol 25:5562–5569

    Article  PubMed  Google Scholar 

  • Lenfant C (2003) Shattuck lecture—clinical research to clinical practice—lost in translation? N Engl J Med 349:868–874

    Article  PubMed  Google Scholar 

  • Memorial Sloan Kettering Cancer Centre (2013) Prostate cancer nomograms. http://nomograms.mskcc.org/Prostate/PreTreatment.aspx. Accessed 1 Mar 2013

  • Miot J, Wagner M, Khoury H et al (2012) Field testing of a multicriteria decision analysis (MCDA) framework for coverage of a screening test for cervical cancer in South Africa. Cost Eff Resour Alloc 10:2

    Article  PubMed Central  PubMed  Google Scholar 

  • Olivotto IA, Bajdik CD, Ravdin PM et al (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23:2716–2725

    Article  PubMed  Google Scholar 

  • Pisters R, Lane DA, Nieuwlaat R et al (2010) A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest 138:1093–1100

    Article  PubMed  Google Scholar 

  • Rothman KJ (2002) Epidemiology: an introduction. Oxford University Press, New York

    Google Scholar 

  • Shakespeare TP, Gebski VJ, Veness MJ et al (2001) Improving interpretation of clinical studies by use of confidence levels, clinical significance curves, and risk-benefit contours. Lancet 357:1349–1353

    Article  CAS  PubMed  Google Scholar 

  • Skanes AC, Healey JS, Cairns JA et al (2012) Focused 2012 update of the Canadian cardiovascular society atrial fibrillation guidelines: recommendations for stroke prevention and rate/rhythm control. Can J Cardiol 28:125–136

    Article  PubMed  Google Scholar 

  • Trotti A, Colevas AD, Setser A et al (2003) CTCAE v3.0: development of a comprehensive grading system for the adverse effects of cancer treatment. Semin Radiat Oncol 13:176–181

    Article  PubMed  Google Scholar 

  • Trotti A, Pajak TF, Gwede CK et al (2007) TAME: development of a new method for summarising adverse events of cancer treatment by the Radiation Therapy Oncology Group. Lancet Oncol 8:613–624

    Article  PubMed  Google Scholar 

  • Yom SS, Liao Z, Liu HH et al (2007) Initial evaluation of treatment-related pneumonitis in advanced-stage non-small-cell lung cancer patients treated with concurrent chemotherapy and intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 68:94–102

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anthony Fyles .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Giuliani, M.E., Hope, A.J., Fyles, A. (2013). Introduction to Decision Tools. In: Nieder, C., Gaspar, L. (eds) Decision Tools for Radiation Oncology. Medical Radiology(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/174_2013_843

Download citation

  • DOI: https://doi.org/10.1007/174_2013_843

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37101-1

  • Online ISBN: 978-3-642-37102-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics