American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. (19th January 2016)

• Record Type: Journal Article
• Authors: Kattan, Michael W.; Hess, Kenneth R.; Amin, Mahul B.; Lu, Ying; Moons, Karl G.M.; Gershenwald, Jeffrey E.; Gimotty, Phyllis A.; Guinney, Justin H.; Halabi, Susan; Lazar, Alexander J.; Mahar, Alyson L.; Patel, Tushar; Sargent, Daniel J.; Weiser, Martin R.; Compton, Carolyn
• Is Part Of: CA. Volume 66:Number 5(2016:Sep./Oct.)
• Page Start: 370
• Page End: 374
• Publication Date: 2016-01-19
• Subjects: decision making -- evidence‐based medicine -- patient preferences -- personalized medicine; Cancer -- Periodicals; Neoplasms -- Periodicals; Neoplasms; 616.99405
• DOI: 10.3322/caac.21339
• Languages: English
• ISSNs: 0007-9235

Abstract

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.