Individualized estimates of overdiagnosis in screen-detected prostate cancer - Abstract

BACKGROUND: The chance that a prostate cancer detected by screening is overdiagnosed (ie, it would not have been detected in the absence of screening) can vary widely depending on the patient's age and tumor characteristics.

The purpose of this study is to use age, Gleason score, and prostate-specific antigen (PSA) level to help inform patients with screen-detected prostate cancers about the chances their cancers were overdiagnosed.

METHODS: A computer microsimulation model of prostate cancer natural history was used to generate virtual life histories in the presence and absence of PSA screening, including an indicator of whether screen-detected cancers are overdiagnosed. A logistic regression model was fit to nonmetastatic patients diagnosed by screening with PSA less than 10ng/mL, and a nomogram was created to predict the individualized risk of overdiagnosis given age, Gleason score, and PSA at diagnosis.

RESULTS: The calibrated microsimulation model closely reproduces observed incidence trends in the Surveillance, Epidemiology, and End Results registries by age, stage, and Gleason score. The fitted logistic regression predicts risks of overdiagnosis among PSA-detected patients with an area under the curve of 0.75. Chances of overdiagnosis range from 2.9% to 88.1%.

CONCLUSIONS: The chances of overdiagnosis vary considerably by age, Gleason score, and PSA at diagnosis. The overdiagnosis nomogram presents tailored estimates of these risks based on patient and tumor information known at diagnosis and can be used to inform decisions about treating PSA-detected prostate cancers.

Written by:
Gulati R, Inoue LY, Gore JL, Katcher J, Etzioni R.   Are you the author?
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA; Department of Biostatistics and Department of Urology, University of Washington, Seattle, WA.

Reference: J Natl Cancer Inst. 2014 Feb 1;106(2):djt367.
doi: 10.1093/jnci/djt367


PubMed Abstract
PMID: 24399850

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