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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.
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.
Afiliação
  • Kattan MW; 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.
  • Hess KR; Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Amin MB; Professor and Chair Emeritus, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Lu Y; 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.
  • Moons KG; Professor of Clinical Epidemiology, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands.
  • Gershenwald JE; 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.
  • Gimotty PA; Professor of Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
  • Guinney JH; Director of Computational Oncology, Sage Bionetworks, Seattle, WA.
  • Halabi S; Professor, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.
  • Lazar AJ; Associate Professor, Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Mahar AL; PhD Candidate, Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.
  • Patel T; Assistant Professor, Department of Pathology, University of Illinois Hospital and Health Sciences System, Chicago, IL.
  • Sargent DJ; Professor of Biostatistics and Oncology, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
  • Weiser MR; Attending Surgeon, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Professor of Surgery, Weill Cornell Medical College, New York, NY.
  • Compton C; 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.
CA Cancer J Clin ; 66(5): 370-4, 2016 09.
Article em En | MEDLINE | ID: mdl-26784705
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;66370-374. © 2016 American Cancer Society.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicina de Precisão / American Cancer Society / Neoplasias Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: CA Cancer J Clin Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicina de Precisão / American Cancer Society / Neoplasias Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: CA Cancer J Clin Ano de publicação: 2016 Tipo de documento: Article