ArticleCancers2023
Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 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
- [Breast (Edinburgh, Scotland) · 2026Trial
- Diagnostic performance ofEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Uptake Patterns of [Cancers · 2026Article
- Diagnostic accuracy of modern imaging following neoadjuvant therapy for breast cancer: a comparative analysis of ultrasound versus [18F] FDG PET/MRI in breast response assessment.Frontiers in oncology · 2026Article
- Contrastive report and multiparametric dual-region magnetic resonance imaging learning for the preoperative prediction of axillary lymph node metastasis in breast cancer.Quantitative imaging in medicine and surgery · 2026Article
- Automated machine learning-based unenhanced radiomics for identification of HER2-low breast cancers using diffusion-weighted imaging.BMC cancer · 2025Article
- Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI.NPJ breast cancer · 2024Article
- More than meets the eye: 2-[European journal of nuclear medicine and molecular imaging · 2024Article
- Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
Abstract
In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced (DCE) imaging (tumor Mean Transit Time, Volume Distribution, Plasma Flow), diffusion-weighted imaging (DWI) (tumor ADCmean), and PET (tumor SUVmax, mean and minimum, SUVmean of ipsilateral breast parenchyma). Manual whole-lesion segmentation was also performed on DCE, T2-weighted, DWI, and PET images, and radiomic features were extracted. The dataset was divided into a training (70%) and a test set (30%). Multi-step feature selection was performed, and a support vector machine classifier was trained and tested for predicting axillary LN status. 13 radiomic features from DCE, DWI, T2-weighted, and PET images were selected for model building. The classifier obtained an accuracy of 79.8 (AUC = 0.798) in the training set and 78.6% (AUC = 0.839), with sensitivity and specificity of 67.9% and 100%, respectively, in the test set. A machine learning-based radiomics model comprising 18F-FDG PET/MRI radiomic features extracted from the primary breast cancer lesions allows high accuracy in non-invasive identification of axillary LN metastasis.
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Registered trials
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