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

members of the AJCC Precision Medicine Core

Research output: Contribution to journalComment/debate

104 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)370-374
Number of pages5
JournalCA Cancer Journal for Clinicians
Volume66
Issue number5
DOIs
StatePublished - Sep 1 2016

Keywords

  • decision making
  • evidence-based medicine
  • patient preferences
  • personalized medicine

ASJC Scopus subject areas

  • Hematology
  • Oncology

Fingerprint Dive into the research topics of 'American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine'. Together they form a unique fingerprint.

  • Cite this