ArticleQuantitative imaging in medicine and surgery2023
Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Assessing the performance of deep learning and hand-crafted radiomics models using MRI and ultrasound in predicting axillary lymph node status in breast cancer: a systematic review and meta-analysis.European radiology · 2026Article
- Augmented Prediction of N Parameter in Breast Cancer: Is It Possible with Shear-Wave Elastography Ultrasound Radiomics?Cancers · 2026Article
- A Noninvasive Predictive Model of Portal Hypertension in Patients with HCC Based on Clinical Features and Intra- and Peritumoral Pre-Fusion Radiomic Features.Journal of hepatocellular carcinoma · 2026Article
- The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer.Translational cancer research · 2025Article
- Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study.Insights into imaging · 2025Article
- Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study.Molecular and clinical oncology · 2025Article
- Enhanced Breast Cancer Diagnosis Using Multimodal Feature Fusion with Radiomics and Transfer Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- 18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.Medical oncology (Northwood, London, England) · 2025Article
- Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.Scientific reports · 2025Article
- Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.The oncologist · 2025Article
- Prediction of axillary lymph node metastasis in T1 breast cancer using diffuse optical tomography, strain elastography and molecular markers.Quantitative imaging in medicine and surgery · 2025Article
- Ultrasound-based radiomics for the evaluation of breast cancer.Frontiers in oncology · 2025Review
- Value of intra- and peritumoral ultrasound radiomics for predicting axillary lymph node burden in breast cancer.Frontiers in oncology · 2025Article
- Retrospective Development of an AI Model Combining Ultrasound and Clinical Data for Pediatric Appendicitis Differentiation.Emergency medicine international · 2025Article
- Prediction of axillary nodal burden using preoperative magnetic resonance imaging scoring in patients with clinically node-negative breast cancer: a retrospective cohort study.Gland surgery · 2024Article
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9 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: This study investigates whether deep learning radiomics of conventional ultrasound images can predict preoperative axillary lymph node (ALN) status in patients with clinical stages T1-2 breast cancer (BC). Methods: This study retrospectively analyzed the preoperative ultrasound data of 892 patients with BC, who were classified into training (n=535), validation (n=178), and test (n=179) cohorts. Linear combinations of the selected features were weighted by their coefficients to obtain the predicted score. Then, deep learning radiomic features were extracted from the ultrasound images to evaluate the ALN status. Receiver-operating characteristic curves were drawn, followed by the calculation of the area under the curve (AUC) to assess the accuracy of the prediction model in predicting axillary lymph node metastasis (ALNM) in the three cohorts. Results: Deep learning radiomics combined with radiomics and clinical parameters was the optimal diagnostic predictor of the ALN status in the absence and presence of ALNM, with the AUC of 0.920 (95% confidence interval: 0.872 and 0.968, respectively). Additionally, this combination could also differentiate low-load ALNM [N + (1-2)] from heavy-load ALNM with ≥3 positive nodes [N + (≥3)] in the test cohort, with the AUC of 0.819 (95% confidence interval: 0.568 and 1.00, respectively). Conclusions: Conclusively, deep learning radiomics of ultrasound images is a non-invasive approach to predicting preoperative ALNM in BC.
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