Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model

ABSTRACT Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations.

Contributors: Fredrik Strand, Gigin Lin, Siddharth Satuluru, Thomas Kim, Imon Banerjee, Judy Gichoya, Hari Trivedi, Constance D. Lehman, Kevin Hughes, David J. Sheedy, Lisa M. Matthis, Bipin Karunakara, Karen E. Hegarty, Silvia Sabino, Thiago B. Silva, Maria C. Evangelista, Renato F. Caron, Bruno Souza, Edmundo C. Mauad, Tal Patalon, Sharon Handelman-Gotlib, Michal Guindy