A New Statistical Framework for Bridging Pathologic Response and Survival in Breast Cancer

Fact checked by: Lajos Pusztai, MD, PhD, Afton Woodward

Bayesian modeling links residual cancer burden shifts to survival, helping neoadjuvant breast cancer trials and FDA approvals predict real benefit.

For nearly 2 decades, oncologists have grappled with a deceptively simple paradox: while achieving a pathologic complete response (pCR) to neoadjuvant chemotherapy is consistently one of the strongest predictors of long-term survival at the individual patient level, improvements in pCR rates across clinical trial arms have not reliably translated into meaningful gains in event-free or overall survival.¹