The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer.

ABSTRACT:

Background

The accurate preoperative assessment of axillary lymph node (ALN) status is critical for therapeutic decision-making in primary breast cancer (BC), yet current methods are either invasive or lack precision. The objective of this study was to investigate the performance of machine learning models based on dynamic contrast-enhanced magnetic resonance imaging (MRI), in conjunction with clinicopathologic data, in predicting different American Joint Committee on Cancer (AJCC) lymph node (N) stages in patients with BC.

Methods

The data of 605 BC patients were retrospectively analyzed and separated into training and test sets. Following dimensionality reduction and feature selection, a predictive model was established via machine learning techniques. Clinicopathologic fe

SUBMITTER: Wang H 

PROVIDER: S-EPMC12776218 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature