Continuous Individualized Risk Index
Continuous Individualized Risk Index (CIRI) (initialism pronounced /ˈsɪri/) is to a set of probabilistic risk models{{Cite web |url=https://www.taylorfrancis.com/books/9780429113079 |title=Bayesian Data Analysis |website=www.taylorfrancis.com |access-date=2019-08-11}} 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){{Cite web|url=https://ciri.stanford.edu/|title=CIRI|website=ciri.stanford.edu|access-date=2019-08-11}}{{Cite journal|last1=Wan|first1=Jonathan C. M.|last2=White|first2=James R.|last3=Diaz|first3=Luis A.|date=2019-07-25|title=Hey CIRI, What's My Prognosis?|journal=Cell|volume=178|issue=3|pages=518–520|doi=10.1016/j.cell.2019.07.005|issn=1097-4172|pmid=31348884|doi-access=free}}{{Cite journal|last1=Kurtz|first1=David M.|last2=Esfahani|first2=Mohammad S.|last3=Scherer|first3=Florian|last4=Soo|first4=Joanne|last5=Jin|first5=Michael C.|last6=Liu|first6=Chih Long|last7=Newman|first7=Aaron M.|last8=Dührsen|first8=Ulrich|last9=Hüttmann|first9=Andreas|date=2019-07-25|title=Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction|journal=Cell|volume=178|issue=3|pages=699–713.e19|doi=10.1016/j.cell.2019.06.011|issn=1097-4172|pmid=31280963|pmc=7380118|doi-access=free}} from Ash Alizadeh's laboratory at Stanford. Inspired by in game win probability models for predicting winners in sports{{Cite web|url=https://fivethirtyeight.com/sports/|archive-url=https://web.archive.org/web/20140318021630/http://fivethirtyeight.com/sports/|url-status=dead|archive-date=March 18, 2014|title=Sports – FiveThirtyEight|language=en-US|access-date=2019-08-11}}{{Cite journal|last=Stern|first=Hal|date=1991-08-01|title=On the Probability of Winning a Football Game|journal=The American Statistician|volume=45|issue=3|pages=179–183|doi=10.1080/00031305.1991.10475798|issn=0003-1305}}{{Cite journal|last1=Lock|first1=Dennis|author2-link=Dan Nettleton|last2=Nettleton|first2=Dan|date=2014|title=Using random forests to estimate win probability before each play of an NFL game|journal=Journal of Quantitative Analysis in Sports|volume=10|issue=2|pages=197–205|doi=10.1515/jqas-2013-0100|s2cid=116921538|issn=1559-0410|url=https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1231&context=stat_las_pubs}} and political elections,{{Cite web|url=https://fivethirtyeight.com/politics/|archive-url=https://web.archive.org/web/20140318015715/http://fivethirtyeight.com/politics/|url-status=dead|archive-date=March 18, 2014|title=Politics – FiveThirtyEight|language=en-US|access-date=2019-08-11}}{{Cite journal|last=Linzer|first=Drew A.|date=2013-03-01|title=Dynamic Bayesian Forecasting of Presidential Elections in the States|journal=Journal of the American Statistical Association|volume=108|issue=501|pages=124–134|doi=10.1080/01621459.2012.737735|s2cid=8787391|issn=0162-1459}} CIRI incorporates serial information obtained throughout a given patient's course to estimate a personalized estimate of various cancer-related risks over time.{{Cite web|url=http://www.medscape.com/viewarticle/915648|title=Sport-Inspired Risk Model Improves Cancer Risk Prediction|website=Medscape|access-date=2019-08-11}}{{Cite web|url=https://cosmosmagazine.com/mathematics/what-are-the-odds-of-beating-cancer|title=What are the odds of beating cancer?|website=Cosmos Magazine|language=en|access-date=2019-08-11}} 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.