Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model.

ABSTRACT:

Background

Neoadjuvant therapy (NAT) regimens for breast cancer are generally determined according to cancer stage and molecular subtypes without fully considering the inter-patient variability, which may lead to inefficiency or overtreatment. Artificial intelligence (AI) may support personalized regimen recommendations by learning the synergistic relationship between pre-NAT individual-patient data, regimens, and corresponding short- or long-term therapy responses.

Methods

In this retrospective study, we collected data from breast cancer patients treated with NAT between 2000 and 2020 from the Netherlands and the USA. Median follow-up times ranged from 3·7 to 4·9 years across molecular subtypes and cohorts. We developed and externally validated a multi-modal model integrati

SUBMITTER: Han L 

PROVIDER: S-EPMC12303063 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature