Radiomics-based MRI models for predicting breast cancer axillary lymph node involvement in comparison with Node-RADS: a proof-of-concept study.

ABSTRACT:

Background

Detection of axillary lymph node (LN) involvement is essential for staging breast cancer and optimizing treatment. This proof-of-concept two-center study explored the feasibility of magnetic resonance imaging (MRI) radiomics-based machine learning models to predict LN involvement and compare their performance with node reporting and data system (Node-RADS).

Materials and methods

We retrospectively included breast cancer patients undergoing preoperative multiparametric MRI and LN dissection (January 2020-September 2024). Stable radiomic features (intraclass correlation coefficient ≥ 0.75) were extracted from contrast-enhanced, subtracted, and T2-weighted sequences. Five machine learning models were trained for binary LN involvement classification, using histopathol

SUBMITTER: Maroncelli R 

PROVIDER: S-EPMC12672992 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature