Mathematical modeling for breast cancer risk assessment. State of the art and role in medicine.

Wendy S. Rubinstein*, Suzanne M. O'Neill, June A. Peters, Laura J. Rittmeyer, Mona P. Stadler

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

19 Scopus citations

Abstract

Women at increased risk of breast cancer have important opportunities for early detection and prevention. There are, however, serious drawbacks to the available interventions. The magnitude of breast cancer risk is a crucial factor in the optimization of medical benefit when considering the efficacy of risk-reduction methods, the adverse effects of intervention, and economic and quality-of-life outcomes. Breast cancer risk assessment has become increasingly quantitative and is amenable to computerization. The assembly of risk factor information into practical, quantitative models for clinical and scientific use is relatively advanced for breast cancer, and represents a paradigm for broader risk management in medicine. Using a case-based approach, we will summarize the major breast cancer risk assessment models, compare and contrast their utility, and illustrate the role of genetic testing in risk management. Important considerations relevant to clinical oncology practice include the role of risk assessment in cancer prevention, the logistics of implementing risk assessment, the ramifications of conveying risk information with limited genetic counseling, and the mechanisms for genetics referral. Medical professionals can embrace new preventive medicine techniques more effectively by utilizing quantitative methods to assess their patients' risks.

Original languageEnglish (US)
Pages (from-to)1082-1094; discussion 1094, 1097-1099
JournalOncology (Williston Park, N.Y.)
Volume16
Issue number8
StatePublished - Aug 2002

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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