ArticleEuropean radiology2018
Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06185946 (Evaluation of Different Imaging Modalities in Regional Nodes Staging in Breast Cancer Patient), which is not on this map. Cited by 147 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluation of Different Imaging Modalities in Regional Nodes Staging in Breast Cancer Patient
Who cites it
147 citing papers in PubMed, 4 syntheses or guidelines pooled 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
- Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.Frontiers in medicine · 2024Pooled it
- The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography.Korean journal of radiology · 2023Trial
- Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning.Thoracic cancer · 2022Trial
- Dynamic nomogram for predicting axillary lymph node metastasis in breast cancer based on MRI analysis and the platelet-to-lymphocyte ratio (PLR).Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.Nature communications · 2026Article
- Predicting chemotherapy response in pediatric lymphoma using pre-treatment contrast-enhanced CT radiomics: A hypothesis-generating study.Abdominal radiology (New York) · 2026Article
- Assessing the diagnostic value of hemodynamic distinctions between axillary lymph nodes and adjacent vessels in breast cancer axillary lymph node metastasis via breast magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- MRI-Based Intratumoral and Multi-Range Peritumoral Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer.International journal of general medicine · 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
- Multiparametric MRI-Based Prediction Model for Sentinel Lymph Node Metastasis in Breast Cancer: Integrating Quantitative Imaging Parameters and Reproductive History.International journal of women's health · 2026Article
- Exploring the predictive value of mammography combined with clinical factors in recurrent metastasis of breast cancer.European journal of medical research · 2025Article
- Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma in Situ to Invasive Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Identifying low-risk breast cancer patients for axillary biopsy exemption: a multimodal preoperative predictive model.European journal of medical research · 2025Article
- Prediction of Breast Cancer Response to Neoadjuvant Therapy with Machine Learning: A Clinical, MRI-Qualitative, and Radiomics Approach.Life (Basel, Switzerland) · 2025Article
- Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer.NPJ digital medicine · 2025Article
- C-C Motif Chemokine Ligand 5 (CCL5) as a Predictive Marker for Non-sentinel Lymph Node Presence in Initial-Stage Breast Cancer with 1-2 Affected Sentinel Lymph Nodes.Molecular biotechnology · 2025Article
- 3.0 T multi-parametric MRI combined with clinical features improve malignancy prediction of BI-RADS 4 lesions and preoperative prediction of Nottingham Prognostic Index.European journal of radiology open · 2025Article
87 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
objectivesTo predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T
methodsWe enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T
resultsFor T
conclusionsFull utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice. KEY POINTS: • SLN biopsy to access breast cancer metastasis has multiple complications. • Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. • We combined T
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.