Contrastive report and multiparametric dual-region magnetic resonance imaging learning for the preoperative prediction of axillary lymph node metastasis in breast cancer.

ABSTRACT:

Background

The accurate prediction of axillary lymph node metastasis (ALNM) is crucial for determining the surgical extent and making treatment decisions for breast cancer patients. However, the method of incorporating clinical diagnostic reports into models to evaluate ALNM is underdeveloped and under validated. This study aimed to investigate the potential of a multimodal deep learning (DL) model that integrates the magnetic resonance imaging (MRI) characteristics of breast tumors and axillary lymph nodes (ALNs) with clinical diagnostic report-derived textual features for accurate ALNM differentiation.

Methods

This study retrospectively enrolled 804 breast cancer patients, of whom, 396 were diagnosed with ALNM and 408 with non-ALNM. First, a vision-language model [Breast A

SUBMITTER: Shen H 

PROVIDER: S-EPMC12780683 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature