Continuous_Individualized_Risk_Index

Continuous Individualized Risk Index

Continuous Individualized Risk Index

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Continuous Individualized Risk Index (CIRI) (initialism pronounced /ˈsɪri/) is to a set of probabilistic risk models[1] utilizing Bayesian statistics for integrating diverse cancer biomarkers over time to produce a unified prediction of outcome risk, as originally described by Kurtz, Esfahani, et al. (2019)[2][3][4] from Ash Alizadeh's laboratory at Stanford. Inspired by in game win probability models for predicting winners in sports[5][6][7] and political elections,[8][9] CIRI incorporates serial information obtained throughout a given patient's course to estimate a personalized estimate of various cancer-related risks over time.[10][11] CIRI models have been developed available for various cancer types, including breast cancer (BRCA), diffuse large B-cell lymphoma (DLBCL), and chronic lymphocytic leukemia (CLL).The serial information integrated can be diverse, including choice of therapy and the associated responses observed, whether using liquid biopsies or radiological studies, pathological and other dynamic measurements.

References

  1. [1]
    "Bayesian Data Analysis". www.taylorfrancis.com. Retrieved 2019-08-11.
  2. [2]
    "CIRI". ciri.stanford.edu. Retrieved 2019-08-11.
  3. [3]
    Wan, Jonathan C. M.; White, James R.; Diaz, Luis A. (2019-07-25). "Hey CIRI, What's My Prognosis?". Cell. 178 (3): 518–520. doi:10.1016/j.cell.2019.07.005. ISSN 1097-4172. PMID 31348884.
  4. [4]
    Kurtz, David M.; Esfahani, Mohammad S.; Scherer, Florian; Soo, Joanne; Jin, Michael C.; Liu, Chih Long; Newman, Aaron M.; Dührsen, Ulrich; Hüttmann, Andreas (2019-07-25). "Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction". Cell. 178 (3): 699–713.e19. doi:10.1016/j.cell.2019.06.011. ISSN 1097-4172. PMC 7380118. PMID 31280963.
  5. [5]
    "Sports – FiveThirtyEight". Retrieved 2019-08-11.
  6. [6]
    Stern, Hal (1991-08-01). "On the Probability of Winning a Football Game". The American Statistician. 45 (3): 179–183. doi:10.1080/00031305.1991.10475798. ISSN 0003-1305.
  7. [7]
    Lock, Dennis; Nettleton, Dan (2014). "Using random forests to estimate win probability before each play of an NFL game". Journal of Quantitative Analysis in Sports. 10 (2): 197–205. doi:10.1515/jqas-2013-0100. ISSN 1559-0410. S2CID 116921538.
  8. [8]
    "Politics – FiveThirtyEight". Retrieved 2019-08-11.
  9. [9]
    Linzer, Drew A. (2013-03-01). "Dynamic Bayesian Forecasting of Presidential Elections in the States". Journal of the American Statistical Association. 108 (501): 124–134. doi:10.1080/01621459.2012.737735. ISSN 0162-1459. S2CID 8787391.
  10. [10]
    "Sport-Inspired Risk Model Improves Cancer Risk Prediction". Medscape. Retrieved 2019-08-11.
  11. [11]
    "What are the odds of beating cancer?". Cosmos Magazine. Retrieved 2019-08-11.
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