Consensus recommendations for advancing breast cancer: risk identification and screening in ethnically diverse younger women

Abstract

A need exists for a breast cancer risk identification paradigm that utilizes relevant demographic, clinical, and other readily obtainable patient-specific data in order to provide individualized cancer risk assessment, direct screening efforts, and detect breast cancer at an early disease stage in historically underserved populations, such as younger women (under age 40) and minority populations, who represent a disproportionate number of military beneficiaries. Recognizing this unique need for military beneficiaries, a consensus panel was convened by the USA TATRC to review available evidence for individualized breast cancer risk assessment and screening in young (< 40), ethnically diverse women with an overall goal of improving care for military beneficiaries. In the process of review and discussion, it was determined to publish our findings as the panel believes that our recommendations have the potential to reduce health disparities in risk assessment, health promotion, disease prevention, and early cancer detection within and in other underserved populations outside of the military. This paper aims to provide clinicians with an overview of the clinical factors, evidence and recommendations that are being used to advance risk assessment and screening for breast cancer in the military.

Keywords: Bayesian Belief Networks; Gail model; breast cancer; machine learning; mammography; personalized medicine; risk assessment; screening.

Figure 1

Evidence-based medicine, levels of evidence

Figure 2

Bayesian Belief Network model, proof of concept for using clinical data from a prospective pilot breast cancer screening study of young women to train, test and cross validate a Bayesian classifier. Primary outcome variable is breast biopsy histopathology (benign, pre-malignant, malignant)