ArticleScientific reports2022
A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- 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 · 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
- Augmented Prediction of N Parameter in Breast Cancer: Is It Possible with Shear-Wave Elastography Ultrasound Radiomics?Cancers · 2026Article
- Prediction of Ki-67 expression in invasive breast cancer with dual-modality radiomics.Scientific reports · 2026Article
- Multi-parametric MRI habitat radiomics with interpretable machine learning for early prediction of axillary lymph node metastasis in triple-negative breast cancer.Frontiers in medicine · 2026Article
- Radiomics-based MRI models for predicting breast cancer axillary lymph node involvement in comparison with Node-RADS: a proof-of-concept study.European radiology experimental · 2025Article
- Ultrasound-based radiomics model for predicting axillary lymph node metastasis of breast cancer.BMC medical imaging · 2025Article
- Gender Medicine in Computed Tomography Radiomics Analysis to Predict Disease Progression in Liver Respectable Colorectal Cancer Patients.Cancer medicine · 2025Article
- Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter study.EClinicalMedicine · 2025Article
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes.Scientific reports · 2025Article
- Value of intra- and peritumoral ultrasound radiomics for predicting axillary lymph node burden in breast cancer.Frontiers in 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
- Ultrasound-based radiomics for the evaluation of breast cancer.Frontiers in oncology · 2025Review
- Micro-computed Tomography in the Evaluation of Eosin-stained Axillary Lymph Node Biopsies of Females Diagnosed with Breast Cancer.Scientific reports · 2024Article
- Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.La Radiologia medica · 2024Article
- Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features.Frontiers in oncology · 2024Article
- An efficient deep learning strategy for accurate and automated detection of breast tumors in ultrasound image datasets.Frontiers in oncology · 2024Article
- Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development.JMIR cancer · 2023Article
- Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer.Scientific reports · 2023Article
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Authors and funding
17 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.
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