ArticleFrontiers in oncology2022
A Nomogram Based on Molecular Biomarkers and Radiomics to Predict Lymph Node Metastasis in Breast Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 17 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
- Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis.Frontiers in oncology · 2024Pooled it
- Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Application Value of a nomogram integrating contrast-enhanced CT radiomics and clinical indicators in evaluating lymph node metastasis in pediatric peripheral neuroblastoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 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
- Development and Validation of a Computed Tomography-based Radiomics Nomogram for Diagnosing Cervical Lymph Node Metastasis in Oropharyngeal Squamous Cell Carcinomas.Advances in radiation oncology · 2025Article
- Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics.Frontiers in oncology · 2025Article
- Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging.Frontiers in oncology · 2025Article
- A dual-modality machine learning precision diagnostic model integrated radiomics and proteomics for breast cancer.Frontiers in immunology · 2025Article
- Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review.World journal of surgical oncology · 2024Review
- Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.Frontiers in oncology · 2024Article
- MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma.Frontiers in oncology · 2022Article
- Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study.Frontiers in oncology · 2022Article
- Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma.Frontiers in oncology · 2022Article
- Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma.Frontiers in oncology · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The aim of this study was to explore the feasibility and efficacy of a non-invasive quantitative imaging evaluation model to assess the lymphatic metastasis of breast cancer based on a radiomics signature constructed using conventional T1-weighted image (T1WI) enhanced MRI and molecular biomarkers. Methods: Patients with breast cancer diagnosed Results: In all, 100 patients were enrolled in this study, including 45 with non-lymph node (LN) metastasis and 55 with LN metastasis. A total of 1,051 texture feature parameters were extracted, and LASSO was used to reduce the dimensionality of the radiomics features. The log(λ) was set to 0.002786, and 19 parameters were retained for the construction of the radiomics tag Rad-score. ROC was used to evaluate the diagnostic efficiency of Rad-score: the area under the ROC curve (AUC) of the Rad-score for identifying non-lymphatic and lymphatic metastases was 0.891 in the training cohort and 0.744 in the validation cohort. With the incorporation of tumor molecular markers, the AUCs of the training cohort and validation cohort of the nomogram were 0.936 and 0.793, respectively, which were notably higher than the AUCs of the clinical parameters in the training and validation cohorts (0.719 and 0.588, respectively). Conclusion: The combined model constructed using the Rad-score and molecular biomarkers can be used as an effective non-invasive method to assess LN metastasis of breast cancer. Furthermore, it can be used to quantitatively evaluate the risk of breast cancer LN metastasis before surgery.
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