ArticleCancer imaging : the official publication of the International Cancer Imaging Society2020
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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21 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 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
- An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.Nature communications · 2026Article
- Radiomics-based Machine Learning Models for Classifying Breast Cancer on Dynamic-Contrast Enhanced MRI through Multi-Observer AnalysisCurrent medical imaging · 2026Article
- Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Radiomics Features of Voxel-Wise DCE-MRI Time-Intensity-Curve Profile Maps.Biomedicines · 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
- Development and validation of a CT-based radiomics nomogram for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Cancer biomarkers : section A of Disease markers · 2025Article
- Delta dual‑region DCE-MRI radiomics from breast masses predicts axillary lymph node response after neoadjuvant therapy for breast cancer.BMC cancer · 2025Article
- Dynamic contrast-enhanced MRI-based radiomics model of intra-tumoral kinetic heterogeneity for predicting breast cancer molecular subtypes.Frontiers in molecular biosciences · 2025Article
- Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.Scientific reports · 2024Article
- Shallow and deep learning classifiers in medical image analysis.European radiology experimental · 2024Review
- CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI.Cancers · 2022Article
- MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer.Frontiers in oncology · 2022Article
- Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI.Frontiers in oncology · 2022Article
- A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer.Frontiers in oncology · 2022Article
- A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients.Insights into imaging · 2021Review
- Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art.Journal of cancer research and clinical oncology · 2021Review
- "Real-world" radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study.Cancers · 2021Article
- Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging.Frontiers in radiology · 2021Review
- A Clinical-Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases.Frontiers in oncology · 2021Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer.
methodsA total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models.
resultsSeven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer.
conclusionsThe model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.
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