Optimal Two-Stage Breast Cancer Screening for Countries with Intermediate or Low Incidence of Breast Cancer
Resource
JOURNAL OF EVALUATION IN CLINICAL PRACTICE v.16 n.6 pp.1345-1352
Journal
JOURNAL OF EVALUATION IN CLINICAL PRACTICE
Journal Volume
v.16
Journal Issue
n.6
Pages
1345-1352
Date Issued
2010
Date
2010
Author(s)
KUO, SHOU-JEN
CHEN, TONY HSIU-HSI
YEN, AMY MING- FANG
CHEN, DAR-REN
CHEN, LI-SHENG
CHEN, LI- SHENG
Abstract
Objectives This study is to identify an optimal cut-off for two-stage breast cancer screening making allowance for variation of the baseline incidence rate and utility values between sensitivity and specificity. Methods We used data from a two-stage breast cancer screening of Taiwanese women aged 50-69 years for whom risk stratification was based on a composite risk score (conventional risk factors); subjects with a risk score greater than the cut-off score were screened using mammography. The Bayesian posterior risk for breast cancer was computed by incorporation of the baseline incidence rate and the risk score. Bayes' maximum utility decision rule was then developed to determine the optimal screening cut- off. Results With a risk score of -9 applied to the current two-stage breast cancer screening programme, we could detect one breast cancer case for every 1406 women. Given different predetermined risks, the selected cut-offs were -9 for 1:1200, -8 for 1:800, -4 for 1:600, -1 for 1:400 and 3 for 1:200 for women aged 50-59 years. When the regret utility ratio of positive predictive value to negative predictive value was set at 1:10, the specificity and sensitivity were 58.5% and 70.4%, respectively, and the optimal cut-off was -5.5. When the ratio was set at 10:1, the sensitivity and specificity were 75.5% and 57.1%, respectively, and the optimal cut-off value was -7.5. Conclusions This study demonstrates that Bayes' maximum utility decision rule can be used to determine optimal cut- off values for two-stage breast cancer screening in countries or areas with lower or intermediate incidence of breast cancer.
Subjects
Bayesian maximum utility decision rule
breast cancer
receiver operating characteristic curve
screening
SDGs