A multicenter study of a predictive model for pathological complete response after neoadjuvant therapy in breast cancer using multimodal digital biomarkers.

ABSTRACT:

Objective

Neoadjuvant therapy (NAT) has become the standard treatment option for patients with locally advanced breast cancer. How to non-invasively screen out patients with pathological complete response (pCR) after NAT has become an urgent world-wide clinical problem. Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.

Methods

In this study, we retrospectively collected longitudinal (pre-NAT and post-NAT) multi-parametric magnetic resonance imaging (MRI) and clinicopathologic data of a total of 1,315 breast cancer patients (clinical stage I-III) who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from Januar

SUBMITTER: Yang Z 

PROVIDER: S-EPMC12780789 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature