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

Adam Yala, Peter G. Mikhael, 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 Karunakaran, Karen E. Hegarty, Silvia Sabino, Thiago B. Silva, Maria C. Evangelista, Renato F. Caron, Bruno SouzaEdmundo C. Mauad, Tal Patalon, Sharon Handelman-Gotlib, Michal Guindy, Regina Barzilay

Research output: Contribution to journalArticlepeer-review


PURPOSEAccurate 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.METHODSWe collected screening mammograms and pathology-confirmed breast cancer outcomes from Massachusetts General Hospital, USA; Novant, USA; Emory, USA; Maccabi-Assuta, Israel; Karolinska, Sweden; Chang Gung Memorial Hospital, Taiwan; and Barretos, Brazil. We evaluated Uno's concordance index for Mirai in predicting risk of breast cancer at one to five years from the mammogram.RESULTSA total of 128,793 mammograms from 62,185 patients were collected across the seven sites, of which 3,815 were followed by a cancer diagnosis within 5 years. Mirai obtained concordance indices of 0.75 (95% CI, 0.72 to 0.78), 0.75 (95% CI, 0.70 to 0.80), 0.77 (95% CI, 0.75 to 0.79), 0.77 (95% CI, 0.73 to 0.81), 0.81 (95% CI, 0.79 to 0.82), 0.79 (95% CI, 0.76 to 0.83), and 0.84 (95% CI, 0.81 to 0.88) at Massachusetts General Hospital, Novant, Emory, Maccabi-Assuta, Karolinska, Chang Gung Memorial Hospital, and Barretos, respectively.CONCLUSIONMirai, a mammography-based risk model, maintained its accuracy across globally diverse test sets from seven hospitals across five countries. This is the broadest validation to date of an AI-based breast cancer model and suggests that the technology can offer broad and equitable improvements in care.

Original languageEnglish (US)
Pages (from-to)1732-1740
Number of pages9
JournalJournal of Clinical Oncology
Issue number16
StatePublished - Jun 1 2022

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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