John T Brinton1,2, R Edward Hendrick3, Brandy M Ringham4, Mieke Kriege5, Deborah H Glueck6. 1. Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA. john.brinton@ucdenver.edu. 2. Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA. john.brinton@ucdenver.edu. 3. Department of Radiology, School of Medicine, University of Colorado Denver, Aurora, CO, USA. 4. Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Denver, Aurora, CO, USA. 5. Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. 6. Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA.
Abstract
BACKGROUND: The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus. METHODS: We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group. RESULTS: A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography. CONCLUSION: Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation.
BACKGROUND: The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus. METHODS: We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group. RESULTS: A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography. CONCLUSION: Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation.
Entities:
Keywords:
Cancer screening; ROC analysis; Risk assessment; Stratified screening
Authors: Pierre O Chappuis; Maria C Katapodi; Chang Ming; Valeria Viassolo; Nicole Probst-Hensch; Ivo D Dinov Journal: Br J Cancer Date: 2020-06-22 Impact factor: 7.640