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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
Open accessAbstract
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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Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Self-citationCollins, G. S., Ogundimu, E. O., & Altman, D. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6787 · Cited by 665
Cited by
AJCC 8th Edition: Colorectal Cancer
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Self-citationMelanoma Staging: American Joint Committee on Cancer (AJCC) 8th Edition and Beyond
Gershenwald, J. E., & Scolyer, R. A. (2018) · Annals of Surgical Oncology · doi:10.1245/s10434-018-6513-7 · Cited by 452
Self-citationGafita, A., Calais, J., Grogan, T. R., Hadaschik, B., Wang, H., Weber, M., Sandhu, S., Kratochwil, C., Esfandiari, R., Tauber, R., et al. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(21)00274-6 · Cited by 271
Machine learning to guide the use of adjuvant therapies for breast cancer
Alaa, A. M., Gurdasani, D., Harris, A. L., Rashbass, J., & van der Schaar, M. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00353-8 · Cited by 55
Halabi, S., Yang, Q., Roy, A., Luo, B., Araujo, J. C., Logothetis, C., Sternberg, C. N., Armstrong, A. J., Carducci, M. A., Chi, K. N., et al. (2023) · Journal of Clinical Oncology · doi:10.1200/jco.22.02661 · Cited by 30
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Metadata
- Authors
- Kattan, M. W., Hess, K. R., Amin, M. B., Lu, Y., Moons, K. G. M., Gershenwald, J. E., Gimotty, P. A., Guinney, J. H., Halabi, S., Lazar, A. J., Mahar, A. L., Patel, T., Sargent, D. J., Weiser, M. R., Compton, C., & Core, M. O. T. A. P. M.
- Venue
- CA: A Cancer Journal for Clinicians
- Publisher
- Wiley
- Published
- 2016-01-19
- Volume
- 66
- Issue
- 5
- Pages
- 370-374
- ISSN
- 0007-9235 (ISSN-L)
- 1542-4863
- OpenAlex
- W2293962048
- Cited by
- 336
- References
- 2
- FWCI
- 19.29
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kattan, M. W., Hess, K. R., Amin, M. B., Lu, Y., Moons, K. G. M., Gershenwald, J. E., Gimotty, P. A., Guinney, J. H., Halabi, S., Lazar, A. J., Mahar, A. L., Patel, T., Sargent, D. J., Weiser, M. R., Compton, C., & Core, M. O. T. A. P. M. (2016). American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. CA: A Cancer Journal for Clinicians. https://doi.org/10.3322/caac.21339
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.