ArticleLa Radiologia medica2024
Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer.
Article in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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30 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- 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
- Habitat-based ultrasound radiomics for preoperative prediction of axillary lymph node metastasis in breast cancer: a two-center study.Gland surgery · 2026Article
- Preoperative CALLY Index and Postoperative Pathological Axillary Nodal Status in Breast Cancer: Association with Nodal Metastasis and Exploratory Analysis of Observed Pathological Nodal Burden.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- B-Mode Ultrasound Radiomics for Differentiating Benign and Malignant Small Hyperechoic Renal Masses: An Exploratory Single-Center Experience.Journal of imaging · 2026Article
- Combining ultrasound and core needle histology imaging Radiopathomics model in predicting axillary lymph node metastasis in triple-negative breast cancer.World journal of surgical oncology · 2026Article
- 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
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Predictive value of ABVS and VTIQ parameters for axillary lymph node metastasis in breast cancer.Frontiers in cell and developmental biology · 2026Article
- Development and External Validation of a Preoperative Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer Using Routine Ultrasound and Core Needle Biopsy Variables.Cancer management and research · 2026Article
- Ultrasound-based radiomics model for predicting axillary lymph node metastasis of breast cancer.BMC medical imaging · 2025Article
- The Role of Multiparametric MRI Radiomics for Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Invasive Breast Cancer: A Comparative Study.Cancer innovation · 2025Article
- A non-invasive preoperative model for predicting sentinel lymph node metastasis in breast cancer using clinical data and MRI.BMC medical imaging · 2025Article
- Integrating multimodal ultrasound imaging for improved radiomics sentinel lymph node assessment in breast cancer.Gland surgery · 2025Article
- Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data.Scientific reports · 2025Article
- Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer.NPJ digital medicine · 2025Article
- Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study.BMC medical imaging · 2025Article
- Predicting lymph node metastasis in papillary thyroid carcinoma: radiomics using two types of ultrasound elastography.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Value of intra- and peritumoral ultrasound radiomics for predicting axillary lymph node burden in breast cancer.Frontiers in oncology · 2025Article
- Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest.Frontiers in neurology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions. MATERIALS AND
methodsA total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson's coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses.
resultsAmong all the radiomics models, the "Mass + Margin3mm" model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882-0.930) and 0.922 (95% CI 0.894-0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816-0.889) in primary cohort, 0.870 (95% CI 0.818-0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781-0.884) in primary cohort, 0.911 (95% CI 0.858-0.964) in validation cohort).
conclusionsThe established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.
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