Optimal two-stage breast cancer screening for countries with intermediate or low incidence of breast cancer

Shou Jen Kuo, Tony Hsiu Hsi Chen, Amy Ming Fang Yen, Dar Ren Chen, Li Sheng Chen

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)1345-1352
Number of pages8
JournalJournal of Evaluation in Clinical Practice
Volume16
Issue number6
DOIs
Publication statusPublished - Dec 2010
Externally publishedYes

Fingerprint

Early Detection of Cancer
Breast Neoplasms
Incidence
Sensitivity and Specificity
Mammography
Emotions

Keywords

  • Bayesian maximum utility decision rule
  • breast cancer
  • receiver operating characteristic curve
  • screening

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health Policy

Cite this

Optimal two-stage breast cancer screening for countries with intermediate or low incidence of breast cancer. / Kuo, Shou Jen; Chen, Tony Hsiu Hsi; Yen, Amy Ming Fang; Chen, Dar Ren; Chen, Li Sheng.

In: Journal of Evaluation in Clinical Practice, Vol. 16, No. 6, 12.2010, p. 1345-1352.

Research output: Contribution to journalArticle

@article{aea4566479224234a08cb8966231c183,
title = "Optimal two-stage breast cancer screening for countries with intermediate or low incidence of breast cancer",
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.",
keywords = "Bayesian maximum utility decision rule, breast cancer, receiver operating characteristic curve, screening",
author = "Kuo, {Shou Jen} and Chen, {Tony Hsiu Hsi} and Yen, {Amy Ming Fang} and Chen, {Dar Ren} and Chen, {Li Sheng}",
year = "2010",
month = "12",
doi = "10.1111/j.1365-2753.2009.01341.x",
language = "English",
volume = "16",
pages = "1345--1352",
journal = "Journal of Evaluation in Clinical Practice",
issn = "1356-1294",
publisher = "Wiley-Blackwell",
number = "6",

}

TY - JOUR

T1 - Optimal two-stage breast cancer screening for countries with intermediate or low incidence of breast cancer

AU - Kuo, Shou Jen

AU - Chen, Tony Hsiu Hsi

AU - Yen, Amy Ming Fang

AU - Chen, Dar Ren

AU - Chen, Li Sheng

PY - 2010/12

Y1 - 2010/12

N2 - 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.

AB - 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.

KW - Bayesian maximum utility decision rule

KW - breast cancer

KW - receiver operating characteristic curve

KW - screening

UR - http://www.scopus.com/inward/record.url?scp=78650616518&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650616518&partnerID=8YFLogxK

U2 - 10.1111/j.1365-2753.2009.01341.x

DO - 10.1111/j.1365-2753.2009.01341.x

M3 - Article

C2 - 20738471

AN - SCOPUS:78650616518

VL - 16

SP - 1345

EP - 1352

JO - Journal of Evaluation in Clinical Practice

JF - Journal of Evaluation in Clinical Practice

SN - 1356-1294

IS - 6

ER -