ArticleThe oncologist2025
Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.
Article in The oncologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Reimagining Nodal Staging in Colorectal Cancer: Toward a Novel Non-Invasive Imaging Approach.Cancers · 2026Review
- Application of Artificial Intelligence in Breast Ultrasound Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- A multimodal ultrasound-based model combining tumor radiomics and axillary lymph node morphologic classification for predicting axillary nodal burden in breast cancer.Frontiers in endocrinology · 2026Article
- Multicenter Validation of an Integrated DCE-MRI Radiomics, Deep Learning, and S-II Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer.Journal of multidisciplinary healthcare · 2026Article
- Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study.Molecular and clinical oncology · 2025Article
- Construction of a prediction model for axillary lymph node metastasis in breast cancer patients based on a multimodal fusion strategy of ultrasound and pathological images.Frontiers in oncology · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundAccurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy.
methodsThis multicenter study included 866 breast cancer patients from 6 hospitals. The data were categorized into training, internal test, external test, and prospective test sets. Deep learning and handcrafted radiomics features were extracted from ultrasound images of primary tumors and lymph nodes. The tumor score and LN score were calculated following feature selection, and a clinical-radiomics model was constructed based on these scores along with clinical-ultrasonic risk factors. The model's performance was validated across the 3 test sets. Additionally, the diagnostic performance of radiologists, with and without model assistance, was evaluated.
resultsThe clinical-radiomics model demonstrated robust discrimination with AUCs of 0.94, 0.92, 0.91, and 0.95 in the training, internal test, external test, and prospective test sets, respectively. It surpassed the clinical model and single score in all sets (P < .05). Decision curve analysis and clinical impact curves validated the clinical utility of the clinical-radiomics model. Moreover, the model significantly improved radiologists' diagnostic accuracy, with AUCs increasing from 0.71 to 0.82 for the junior radiologist and from 0.75 to 0.85 for the senior radiologist.
conclusionsThe clinical-radiomics model effectively predicts ALNM in breast cancer patients using noninvasive ultrasound features. Additionally, it enhances radiologists' diagnostic accuracy, potentially optimizing resource allocation in breast cancer management.
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