Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

Jeremy S Paige; Christoph I Lee; Pin-Chieh Wang; William Hsu; Adam R Brentnall; Anne C Hoyt; Arash Naeim; Joann G Elmore

doi:10.1007/s11606-023-08043-4

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

J Gen Intern Med. 2023 Aug;38(11):2584-2592. doi: 10.1007/s11606-023-08043-4. Epub 2023 Feb 7.

Authors

Jeremy S Paige¹, Christoph I Lee², Pin-Chieh Wang³, William Hsu¹, Adam R Brentnall⁴, Anne C Hoyt¹, Arash Naeim⁵, Joann G Elmore⁶

Affiliations

¹ Department of Radiology, University of California, Los Angeles, CA, USA.
² Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
³ Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, and Office of Health Informatics and Analytics, University of California, Los Angeles, Los Angeles, USA.
⁴ Centre for Evaluation and Methods, Wolfson Institute of Population Health, Charterhouse Square, Queen Mary University of London, London, UK.
⁵ Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
⁶ Department of Medicine, Division of General Internal Medicine and Health Services Research and the National Clinician Scholars Program, David Geffen School of Medicine, University of California, Los Angeles, 1100 Glendon Ave, Ste. 900, Los Angeles, CA, 90024, USA. jelmore@mednet.ucla.edu.

Abstract

Background: Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain.

Objective: To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer.

Design: Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model.

Subjects: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome.

Main measures: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%).

Key results: A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level.

Conclusions: Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Keywords: breast cancer; chemoprevention; mammography; risk models; screening.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / epidemiology
Early Detection of Cancer
Female
Humans
Mammography / adverse effects
Quality of Life
Risk Assessment
Risk Factors

Grants and funding

R37 CA240403/CA/NCI NIH HHS/United States