BACKGROUND: A 2% threshold, traditionally used as a level above which breast biopsy recommended, has been generalized to all patients from several specific situations analyzed in the literature. We use a sequential decision analytic model considering clinical and mammography features to determine the optimal general threshold for image guided breast biopsy and the sensitivity of this threshold to variation of these features. METHODOLOGY/PRINCIPAL FINDINGS: We built a decision analytical model called a Markov Decision Process (MDP) model, which determines the optimal threshold of breast cancer risk to perform breast biopsy in order to maximize a patient's total quality-adjusted life years (QALYs). The optimal biopsy threshold is determined based on a patient's probability of breast cancer estimated by a logistic regression model (LRM) which uses demographic risk factors (age, family history, and hormone use) and mammographic findings (described using the established lexicon-BI-RADS). We estimate the MDP model's parameters using SEER data (prevalence of invasive vs. in situ disease, stage at diagnosis, and survival), US life tables (all cause mortality), and the medical literature (biopsy disutility and treatment efficacy) to determine the optimal "base case" risk threshold for breast biopsy and perform sensitivity analysis. The base case MDP model reveals that 2% is the optimal threshold for breast biopsy for patients between 42 and 75 however the thresholds below age 42 is lower (1%) and above age 75 is higher (range of 3-5%). Our sensitivity analysis reveals that the optimal biopsy threshold varies most notably with changes in age and disutility of biopsy. CONCLUSIONS/SIGNIFICANCE: Our MDP model validates the 2% threshold currently used for biopsy but shows this optimal threshold varies substantially with patient age and biopsy disutility.
Burnside, Elizabeth S Chhatwal, Jagpreet Alagoz, Oguzhan K07-CA114181/CA/NCI NIH HHS/United States R01 LM010921/LM/NLM NIH HHS/United States R01-CA127379/CA/NCI NIH HHS/United States R21-CA129393/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Validation Studies United States PLoS One. 2012;7(11):e48820. doi: 10.1371/journal.pone.0048820. Epub 2012 Nov 7.