Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.

Journal: Breast cancer research : BCR
Published Date: Jun 20, 2019

Abstract

BACKGROUND: Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53-0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods-the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models.

Authors

  • Chang Ming
    Nursing Science, Faculty of Medicine, University of Basel, Bernoullistrasse 28, Room 118, 4056, Basel, Switzerland. chang.ming@unibas.ch.
  • Valeria Viassolo
    Oncogenetics and Cancer Prevention, Geneva University Hospitals, Geneva, Switzerland.
  • Nicole Probst-Hensch
    Swiss Tropical and Public Health Institute Basel, Department of Epidemiology and Public Health, University of Basel, Basel, Switzerland.
  • Pierre O Chappuis
    Oncogenetics and Cancer Prevention, Geneva University Hospitals, Geneva, Switzerland.
  • Ivo D Dinov
    Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Statistics Online Computational Resource, Department of Health Behavior and Biological, University of Michigan, Ann Arbor, MI, USA.
  • Maria C Katapodi
    Nursing Science, Faculty of Medicine, University of Basel, Bernoullistrasse 28, Room 118, 4056, Basel, Switzerland.

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