Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.

ABSTRACT:

Background

Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data.

Methods

MRI-based DLS was constructed (radiogenomic training dataset, n =

SUBMITTER: Duan J 

PROVIDER: S-EPMC10726892 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature