{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T00:43:53Z","timestamp":1792889033156,"version":"4.5.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024. Multiple sequence images from MG and MRI were selected, and regions of interest in the lesions were delineated. Radiomics and deep learning (3D-Resnet18) features were extracted and fused. The samples were randomly divided into training and test sets in a 7:3 ratio. Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator (LASSO) regression model, and other methods. Various machine learning algorithms were used to construct radiomics, deep learning, and combined models. These models were visualized and evaluated for performance using receiver operating characteristic curves, area under the curve (AUC), calibration curves, and decision curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The highest AUCs in the test set were achieved using radiomics-logistic regression (AUC = 0.759), deep learning-multilayer perceptron (MLP) (AUC = 0.712), and combined-MLP models (AUC = 0.846). The MLP model demonstrated strong classification performance, with the combined model (AUC = 0.846) outperforming both the radiomics (AUC = 0.756) and deep learning (AUC = 0.712) models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multimodal radiomics and deep learning models developed in this study, incorporating various machine learning algorithms, offer significant value for the preoperative prediction of ALNM in BC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2024.1482020","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T01:23:44Z","timestamp":1734053024000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning 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