Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2025-01-01T13:51:24.386Z Has data issue: false hasContentIssue false

7 - Optimization of Biomarker-Based Prostate Cancer Screening Policies

from Part II - Optimizing Healthcare Systems

Published online by Cambridge University Press:  21 April 2022

By
Christine L. Barnett  and
Brian T. Denton
Edited by
Sze-chuan Suen ,
David Scheinker  and
Eva Enns
Sze-chuan Suen
Affiliation:
University of Southern California
David Scheinker
Affiliation:
Stanford University, California
Eva Enns
Affiliation:
University of Minnesota
Get access

Summary

Mathematical models may be used to optimize the decision of when to screen for cancer and how invasive a test to use, for example a biopsy or a biomarker. Partially observable Markov decision process (POMDP) models may be used to optimize screening decisions based on a patient's belief state, which is calculated using Bayesian updating and comprises a patient's complete history of biomarker test results. POMDPs can be used to determine how, if at all, biomarkers should be used for cancer screening in order to maximize quality-adjusted life years, a population health measure of disease burden that incorporates both the quality and quantity of life.

Keywords

Partially observable Markov decision processPOMDPscreeningcancer
Type
Chapter
Information
Artificial Intelligence for Healthcare
Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
 , pp. 141 - 158
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andriole, GL, Crawford, ED, Grubb, RL III, et al. Mortality results from a randomized prostate-cancer screening trial. New England Journal of Medicine. 2009 Mar 26;360(13):1310–1319.Google Scholar
Andriole, GL, Crawford, ED, Grubb, RL III, et al. Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. Journal of the National Cancer Institute. 2012 Jan 6;104(2):125–132.Google Scholar
Arias, E. United States life tables 2006. National Vital Statistics Reports. 2010;58(21):1–40.Google ScholarPubMed
Ayer, T, Alagz, O, Stout, NK. A POMDP approach to personalize mammography screening decisions. Operations Research. 2012; 60(5):1019–1034.Google Scholar
Barnett, CL, Tomlins, SA, Underwood, DJ, et al. Two-stage biomarker protocols for improving the precision of early detection of prostate cancer. Medical Decision Making. 2017 Oct;37(7):815–826.Google Scholar
Carter, HB, Albertsen, PC, Barry, MJ, et al. Early detection of prostate cancer: AUA Guideline. The Journal of Urology. 2013 Aug;190(2):419–426.Google Scholar
Cassandra, AR, Kaelbling, LP, Littman, ML. Acting optimally in partially observable stochastic domains. Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, 1994: 1023–1028.Google Scholar
Draisma, G, Boer, R, Otto, SJ, et al. Lead times and overdetection due to prostate-specific antigen screening: estimates from the European Randomized Study of Screening for Prostate Cancer. Journal of the National Cancer Institute. 2003 Jun 18;95(12):868–878.CrossRefGoogle ScholarPubMed
Erenay, FS, Alagoz, O, Said, A. Optimizing colonoscopy screening for colorectal cancer prevention and surveillance. Manufacturing & Service Operations Management. 2014; 16(3):381–400.Google Scholar
Haas, GP, Delongchamps, NB, Jones, RF, et al. Needle biopsies on autopsy prostates: sensitivity of cancer detection based on true prevalence. Journal of the National Cancer Institute. 2007 Oct 3;99(19):1484–1489.Google Scholar
Hamdy, FC, Donovan, JL, Lane, JA, et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. New England Journal of Medicine. 2016 Oct 13;375(15):1415–1424.Google Scholar
Hartigan, JA, Wong, MA. Algorithm AS 136: a k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics). 1979 Jan 1;28(1):100–108.Google Scholar
Heijnsdijk, EA, Wever, EM, Auvinen, A, et al. Quality-of-life effects of prostate-specific antigen screening. New England Journal of Medicine. 2012 Aug 16;367(7):595–605.Google Scholar
Liu, J, Womble, PR, Merdan, S, et al Factors influencing selection of active surveillance for localized prostate cancer. Urology. 2015 Nov 1;86(5):901–905.Google Scholar
Monahan, G. A survey of partially observable Markov decision processes: theory, models, and algorithms. Management Science. 1982; 28(1):1–16.CrossRefGoogle Scholar
Moyer, VA. Screening for prostate cancer: US Preventive Services Task Force recommendation statement. Annals of Internal Medicine. 2012; 157(2):120–134.Google Scholar
Nguyen, CT, Yu, C, Moussa, A, Kattan, MW, Jones, JS. Performance of prostate cancer prevention trial risk calculator in a contemporary cohort screened for prostate cancer and diagnosed by extended prostate biopsy. The Journal of Urology. 2010 Feb;183(2):529–533.CrossRefGoogle Scholar
Ries, LA, Young, JL Jr, Keel, GE, et al. Cancer survival among adults: US SEER program, 1988–2001. Patient and Tumor Characteristics SEER Survival Monograph Publication. 2007:07-6215.Google Scholar
Roehl, KA, Han, M, Ramos, CG, Antenor, JA, Catalona, WJ. Cancer progression and survival rates following anatomical radical retropubic prostatectomy in 3,478 consecutive patients: long-term results. The Journal of Urology. 2004 Sep;172(3):910–914.Google Scholar
Ross, KS, Carter, HB, Pearson, JD, Guess, HA. Comparative efficiency of prostate-specific antigen screening strategies for prostate cancer detection. JAMA. 2000 Sep 20;284(11):1399–1405.CrossRefGoogle ScholarPubMed
Sanders, GD, Neumann, PJ, Basu, A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA. 2016 Sep 13;316(10):1093–1103.CrossRefGoogle ScholarPubMed
Schröder, FH, Hugosson, J, Roobol, MJ, et al. Screening and prostate-cancer mortality in a randomized European study. New England Journal of Medicine. 2009 Mar 26;360(13):1320–1328.Google Scholar
Schröder, FH, Hugosson, J, Roobol, MJ, et al. Prostate-cancer mortality at 11 years of follow-up. New England Journal of Medicine. 2012 Mar 15;366(11):981–990.Google Scholar
Schröder, FH, Hugosson, J, Roobol, MJ, et al. Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. The Lancet. 2014 Dec 6;384(9959):2027–2035.Google Scholar
Simmons Ivy, J, Black Nembhard, H, Baran, K. Quantifying the impact of variability and noise on patient outcomes in breast cancer decision making. Quality Engineering. 2009; 21(3):319–334.CrossRefGoogle Scholar
Smallwood, RD, Sondik, EJ. The optimal control of partially observable Markov processes over a finite horizon. Operations Research. 1973 Oct;21(5):1071–1088.CrossRefGoogle Scholar
Thompson, IM, Ankerst, DP, Chi, C, et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. Journal of the National Cancer Institute. 2006 Apr 19;98(8):529–534.CrossRefGoogle ScholarPubMed
Tomlins, SA, Day, JR, Lonigro, RJ, et al. Urine TMPRSS2:ERG plus PCA3 for individualized prostate cancer risk assessment. European Urology. 2016; 70(1):45–53.Google Scholar
Zhang, J, Denton, BT, Balasubramanian, H, Shah, ND, Inman, BA. Optimization of prostate biopsy referral decisions. Manufacturing & Service Operations Management. 2012; 14(4):529–547.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×