ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1546229
Radiomics Models to Predict Axillary Lymph Node Metastasis in Breast Cancer and Analysis of the Biological Significance of Radiomic Features
Provisionally accepted
- 1Guangdong Medical University, Zhanjiang, Guangdong, China
- 2Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, Zhejiang, China
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Objectives: To explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.: The 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the training (n=224), testing (n=96), and validation (n=59) cohorts. The Cancer Imaging Archive-The Cancer Genome Atlas (TCIA-TCGA) group included 107 patients with breast cancer. A total of 1888 intratumoral and peritumoral radiomics features were extracted from DCE-MRI sequences. Radiomics models were established using a multivariate regression algorithm for each region and their combinations. Clinical and combined nomogram models integrating the Radscore with clinical risk factors were constructed. The biological significance of the radiomic features was analyzed by combining the TCIA database. Results: The area under the ROC curve (AUC) of radiomics model in the external validation was 0.760 (95% confidence interval [CI]: 0.626-0.874). The performance of the nomogram combined model (AUC: 0.818; 95% CI:0.702-0.916) surpassed those of both the radiomics and clinical models (AUC: 0.753; 95% CI: 0.630-0.869). Additionally, the DCA results demonstrated the usefulness of the radiomics and nomogram model. Conclusion: MRI-based radiomics has the potential to predict the ALNM status in patients with invasive breast cancer. Additionally, radiogenomic analysis demonstrated a correlation between radiomic features and the immune microenvironment.
Keywords: Axillary lymph node metastasis, Radiomics, Biological significance, Cancer Imaging Archive-The Cancer Genome Atlas, breast cancer
Received: 23 Dec 2024; Accepted: 28 May 2025.
Copyright: © 2025 Li, Hong, Lu, Liu, Lin and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hongfa Xu, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China
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