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Related Experiment Video

Updated: May 22, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Risk Models to Predict Screen-Detected and Interval Breast Cancers in Population Mammography Screening Participants.

Naomi Noguchi1, Armando Teixeira-Pinto1, Michael Luke Marinovich2

  • 1Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia.

Cancers
|March 13, 2025
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Summary

Older age and symptoms strongly predict screen-detected breast cancers, while dense breasts and symptoms predict interval breast cancers. These conventional risk factors offer moderate accuracy in identifying women at risk.

Keywords:
breast neoplasmsearly detection of cancerinterval cancermammographymass screeningrisk factors

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Area of Science:

  • Oncology
  • Radiology
  • Public Health

Background:

  • Breast cancer screening programs aim to detect cancers early.
  • Identifying women at high risk for screen-detected and interval breast cancers is crucial for optimizing screening strategies.
  • Conventional risk factors are routinely collected but their predictive power for different breast cancer subtypes requires evaluation.

Purpose of the Study:

  • To assess the accuracy of conventional risk factors in identifying women at risk for screen-detected and interval breast cancers.
  • To evaluate the predictive performance of age, symptoms, family history, personal history of breast cancer, dense breasts, and hormone replacement therapy use.
  • To compare the predictive models for screen-detected, advanced screen-detected, and interval breast cancers.

Main Methods:

  • Analysis of 1,026,137 mammography screening examinations from 323,082 women (July 2007-June 2017).
  • Development of multivariable models to predict screen-detected, advanced screen-detected (≥pT2), and interval breast cancers.
  • Calculation of odds ratios (ORs) and hazard ratios (HRs) for various risk factors and assessment of model discrimination using areas under the receiver operating characteristic curves (AUC).

Main Results:

  • Older age (≥60 years) and symptoms were significant predictors for screen-detected cancers (ORs 2.56-3.60 and 7.44, respectively), with stronger associations for advanced cancers.
  • Dense breasts (HR 2.36) and symptoms (HR 3.27) were key predictors for interval cancers.
  • First-degree family history, personal history of breast cancer, and recent hormone replacement therapy use were also associated with increased risk for specific cancer types.
  • AUC values ranged from 0.643 to 0.706, indicating moderate discrimination for all models.

Conclusions:

  • Conventional risk factors, particularly older age and symptoms, effectively predict screen-detected breast cancers.
  • Dense breasts and symptoms are the strongest predictors for interval breast cancers.
  • The developed models demonstrate moderate predictive accuracy, comparable to existing established models, suggesting potential for risk stratification in screening programs.