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American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine.

Publication ,  Journal Article
Kattan, MW; Hess, KR; Amin, MB; Lu, Y; Moons, KGM; Gershenwald, JE; Gimotty, PA; Guinney, JH; Halabi, S; Lazar, AJ; Mahar, AL; Patel, T ...
Published in: CA Cancer J Clin
September 2016

The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for more personalized probabilistic predictions than those delivered by ordinal staging systems, particularly through the use of accurate risk models or calculators. However, judging the quality and acceptability of a risk model is complex. The AJCC Precision Medicine Core conducted a 2-day meeting to discuss characteristics necessary for a quality risk model in cancer patients. More specifically, the committee established inclusion and exclusion criteria necessary for a risk model to potentially be endorsed by the AJCC. This committee reviewed and discussed relevant literature before creating a checklist unique to this need of AJCC risk model endorsement. The committee identified 13 inclusion and 3 exclusion criteria for AJCC risk model endorsement in cancer. The emphasis centered on performance metrics, implementation clarity, and clinical relevance. The facilitation of personalized probabilistic predictions for cancer patients holds tremendous promise, and these criteria will hopefully greatly accelerate this process. Moreover, these criteria might be useful for a general audience when trying to judge the potential applicability of a published risk model in any clinical domain. CA Cancer J Clin 2016;66:370-374. © 2016 American Cancer Society.

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Published In

CA Cancer J Clin

DOI

EISSN

1542-4863

Publication Date

September 2016

Volume

66

Issue

5

Start / End Page

370 / 374

Location

United States

Related Subject Headings

  • United States
  • Risk
  • Prognosis
  • Precision Medicine
  • Oncology & Carcinogenesis
  • Neoplasms
  • Neoplasm Staging
  • Humans
  • Evidence-Based Medicine
  • Decision Making
 

Citation

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Chicago
ICMJE
MLA
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Kattan, M. W., Hess, K. R., Amin, M. B., Lu, Y., Moons, K. G. M., Gershenwald, J. E., … members of the AJCC Precision Medicine Core. (2016). American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. CA Cancer J Clin, 66(5), 370–374. https://doi.org/10.3322/caac.21339
Kattan, Michael W., Kenneth R. Hess, Mahul B. Amin, Ying Lu, Karl G. M. Moons, Jeffrey E. Gershenwald, Phyllis A. Gimotty, et al. “American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine.CA Cancer J Clin 66, no. 5 (September 2016): 370–74. https://doi.org/10.3322/caac.21339.
Kattan MW, Hess KR, Amin MB, Lu Y, Moons KGM, Gershenwald JE, et al. American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. CA Cancer J Clin. 2016 Sep;66(5):370–4.
Kattan, Michael W., et al. “American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine.CA Cancer J Clin, vol. 66, no. 5, Sept. 2016, pp. 370–74. Pubmed, doi:10.3322/caac.21339.
Kattan MW, Hess KR, Amin MB, Lu Y, Moons KGM, Gershenwald JE, Gimotty PA, Guinney JH, Halabi S, Lazar AJ, Mahar AL, Patel T, Sargent DJ, Weiser MR, Compton C, members of the AJCC Precision Medicine Core. American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. CA Cancer J Clin. 2016 Sep;66(5):370–374.
Journal cover image

Published In

CA Cancer J Clin

DOI

EISSN

1542-4863

Publication Date

September 2016

Volume

66

Issue

5

Start / End Page

370 / 374

Location

United States

Related Subject Headings

  • United States
  • Risk
  • Prognosis
  • Precision Medicine
  • Oncology & Carcinogenesis
  • Neoplasms
  • Neoplasm Staging
  • Humans
  • Evidence-Based Medicine
  • Decision Making