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

Michael W. Kattan(Cleveland Clinic Lerner College of Medicine), Kenneth R. Hess(The University of Texas MD Anderson Cancer Center), Mahul B. Amin(Cedars-Sinai Medical Center), Ying Lü(Stanford University), Karl G.M. Moons(University Medical Center Utrecht), Jeffrey E. Gershenwald(The University of Texas MD Anderson Cancer Center), Phyllis A. Gimotty(Cancer Research And Biostatistics), Justin Guinney(Sage Bionetworks), Susan Halabi(Duke University), Alexander J. Lazar(The University of Texas MD Anderson Cancer Center), Alyson Mahar(Queen's University), Tushar Patel(University of Illinois Chicago), Daniel J. Sargent(Mayo Clinic), Martin R. Weiser(Memorial Sloan Kettering Cancer Center), Carolyn C. Compton(Mayo Clinic), members of the AJCC Precision Medicine Core
CA A Cancer Journal for Clinicians
January 19, 2016
Cited by 353Open Access
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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. © 2016 American Cancer Society.


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