Continuous Individualized Risk Index Explained

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.

Notes and References

  1. Web site: Bayesian Data Analysis . www.taylorfrancis.com . 2019-08-11.
  2. Web site: CIRI. ciri.stanford.edu. 2019-08-11.
  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. 10.1016/j.cell.2019.07.005. 1097-4172. 31348884. free.
  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. 10.1016/j.cell.2019.06.011. 1097-4172. 31280963. 7380118. free.
  5. Web site: Sports – FiveThirtyEight. en-US. 2019-08-11.
  6. Stern. Hal. 1991-08-01. On the Probability of Winning a Football Game. The American Statistician. 45. 3. 179–183. 10.1080/00031305.1991.10475798. 0003-1305.
  7. Lock. Dennis. Dan Nettleton. 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. 10.1515/jqas-2013-0100. 116921538. 1559-0410.
  8. Web site: Politics – FiveThirtyEight. en-US. 2019-08-11.
  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. 10.1080/01621459.2012.737735. 8787391. 0162-1459.
  10. Web site: Sport-Inspired Risk Model Improves Cancer Risk Prediction. Medscape. 2019-08-11.
  11. Web site: What are the odds of beating cancer?. Cosmos Magazine. en. 2019-08-11.

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