AUTHOR=Wang Si-Rui , Tian Feng , Zhu Tong , Cao Chun-Li , Wang Jin-Li , Li Wen-Xiao , Li Jun , Hou Ji-Xue 

TITLE=Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1548888

DOI=10.3389/fendo.2025.1548888

ISSN=1664-2392

ABSTRACT=ObjectiveThis study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.MethodsA total of 131 breast cancer patients with axillary lymph node metastasis (ALNM) were enrolled between June 2019 and September 2024. Patients were divided into low (n=79) and high (n=52) axillary lymph node burden (ALNB) groups. They were further split into training (n=92) and validation (n=39) cohorts. Intratumoral and peritumoral features were analyzed using the maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) methods. Six machine learning models were evaluated, and a combined clinical-radiomics model was built.ResultsThe combined logistic regression model exhibited superior diagnostic performance for high axillary lymph node burden, with areas under the ROC curve (AUC) of 0.857 in the training cohort and 0.820 in the validation cohort, outperforming individual models. The model balanced sensitivity and specificity well at a 52% cutoff value. A nomogram provided a practical risk assessment tool for clinicians.ConclusionThe combined clinical-radiomics model showed excellent predictive ability and may aid in optimizing management and treatment decisions for breast cancer patients.