ReviewEuropean journal of radiology2015
Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.
Review in European journal of radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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.
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Who cites it
18 citing papers in PubMed, 30 citations in OpenAlex.
- 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
- Kinetic and radiomic features on DCE-MRI as a predictor for axillary lymph node metastasis burden in T1 and T2 stage breast cancer.Frontiers in oncology · 2025Article
- A Nomogram for Identifying HR+/Her2- Breast Cancer Patients with Positive Sentinel Lymph Nodes and Omitted Axillary Lymph Node Dissection Who Need Abemaciclib Therapy.Medical science monitor : international medical journal of experimental and clinical research · 2023Article
- Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma.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
- Clinical Artificial Intelligence Applications: Breast Imaging.Radiologic clinics of North America · 2021Review
- Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- PET/CT of breast cancer regional nodal recurrences: an evaluation of contouring atlases.Radiation oncology (London, England) · 2020Article
- A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer.Frontiers in oncology · 2020Article
- Texture Analysis of Multi-Shot Echo-planar Diffusion-Weighted Imaging in Head and Neck Squamous Cell Carcinoma: The Diagnostic Value for Nodal Metastasis.Journal of clinical medicine · 2019Article
- Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI.Scientific reports · 2019Article
- Heterogeneous Enhancement Pattern in DCE-MRI Reveals the Morphology of Normal Lymph Nodes: An Experimental Study.Contrast media & molecular imaging · 2019Article
- Accuracy of a nomogram to predict the survival benefit of surgical axillary staging in T1 breast cancer patients.Medicine · 2018Article
- Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2018Article
- Use of Quantitative Morphological and Functional Features for Assessment of Axillary Lymph Node in Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging.BioMed research international · 2018Article
- Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.Journal of magnetic resonance imaging : JMRI · 2016Article
- MRI sequences for the detection of individual lymph nodes in regional breast radiotherapy planning.The British journal of radiology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo assess the performance of computer extracted feature analysis of dynamic contrast enhanced (DCE) magnetic resonance images (MRI) of axillary lymph nodes. To determine which quantitative features best predict nodal metastasis.
methodsThis institutional board-approved HIPAA compliant study, in which informed patient consent was waived, collected enhanced T1 images of the axilla from patients with breast cancer. Lesion segmentation and feature analysis were performed on 192 nodes using a laboratory-developed quantitative image analysis (QIA) workstation. The importance of 28 features were assessed. Classification used the features as input to a neural net classifier in a leave-one-case-out cross-validation and evaluated with receiver operating characteristic (ROC) analysis.
resultsThe area under the ROC curve (AUC) values for features in the task of distinguishing between positive and negative nodes ranged from just over 0.50 to 0.70. Five features yielded AUCs greater than 0.65: two morphological and three textural features. In cross-validation, the neural net classifier obtained an AUC of 0.88 (SE 0.03) for the task of distinguishing between positive and negative nodes.
conclusionQIA of DCE MRI demonstrated promising performance in discriminating between positive and negative axillary nodes.
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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.