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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.

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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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          Author and article information

          Journal
          CA Cancer J Clin
          CA: a cancer journal for clinicians
          American Cancer Society
          1542-4863
          0007-9235
          Sep 2016
          : 66
          : 5
          Affiliations
          [1 ] Chairman, Department of Quantitative Health Sciences in the Lerner Research Institute, Cleveland Clinic, Cleveland, OH; Professor of Medicine, Epidemiology, and Biostatistics, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH.
          [2 ] Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
          [3 ] Professor and Chair Emeritus, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
          [4 ] Professor of Biomedical Data Science (Statistics), Department of Biomedical Data Science and Department of Health Research and Policy, Stanford University, Stanford, CA; Director, US VA Cooperative Studies Program, Palo Alto, CA.
          [5 ] Professor of Clinical Epidemiology, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands.
          [6 ] Dr. John M. Skibber Professor, Department of Surgical Oncology, and Professor, Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
          [7 ] Professor of Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
          [8 ] Director of Computational Oncology, Sage Bionetworks, Seattle, WA.
          [9 ] Professor, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.
          [10 ] Associate Professor, Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.
          [11 ] PhD Candidate, Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.
          [12 ] Assistant Professor, Department of Pathology, University of Illinois Hospital and Health Sciences System, Chicago, IL.
          [13 ] Professor of Biostatistics and Oncology, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
          [14 ] Attending Surgeon, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Professor of Surgery, Weill Cornell Medical College, New York, NY.
          [15 ] Professor, Life Science, Arizona State University, Tempe, AZ; Professor, Laboratory Medicine and Pathology, Mayo Clinic School of Medicine, Rochester, MN; Chief Medical Officer, National Biomarker Development Alliance, Scottsdale, AZ.
          Article
          NIHMS768803
          10.3322/caac.21339
          4955656
          26784705
          de2a2967-9bbf-4c27-9696-41146a127b20
          History

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

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