ArticleJournal of magnetic resonance imaging : JMRI2019
Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Augmented Prediction of N Parameter in Breast Cancer: Is It Possible with Shear-Wave Elastography Ultrasound Radiomics?Cancers · 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
- MRI-Based Intratumoral and Multi-Range Peritumoral Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer.International journal of general medicine · 2026Article
- 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
- Article
- DCE-MRI-based machine learning model for predicting axillary lymph node metastasis in breast cancer.Gland surgery · 2025Article
- Clinical-radiomics model for predicting internal mammary lymph node metastasis in operable breast cancer patients.Frontiers in oncology · 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
- Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.Scientific reports · 2024Article
- Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer.NPJ breast cancer · 2024Article
- Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis.Annals of surgical oncology · 2024Article
- Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.Frontiers in oncology · 2024Article
- Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring.AJR. American journal of roentgenology · 2024Review
- Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk.Radiology · 2024Article
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.Journal of digital imaging · 2023Article
- Abbreviated MRI for Comprehensive Regional Lymph Node Staging during Pre-Operative Breast MRI.Cancers · 2023Article
- CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI.Cancers · 2022Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundThe axillary lymph node status is critical for breast cancer staging and individualized treatment planning. PURPOSE: To assess the effect of determining axillary lymph node (ALN) metastasis by breast MRI-derived radiomic signatures, and compare the discriminating abilities of different MR sequences. STUDY TYPE: Retrospective. POPULATION: In all, 120 breast cancer patients, 59 with ALN metastasis and 61 without metastasis, all confirmed by pathology. FIELD STRENGTH/SEQUENCE: 3 .0T scanner with T ASSESSMENT: Typical morphological and texture features of the segmented tumor were extracted from four sequences, ie, T STATISTICAL TESTS: Mann-Whitney U-test, Fisher's exact test, least absolute shrinkage selection operator (LASSO) regression, and receiver operating characteristic analysis were performed.
resultsThe accuracy/AUC of the four sequences was 79%/0.87, 77%/0.85, 74%/0.79, and 79%/0.85 for the T DATA
conclusionBreast tumor's radiomic signatures from preoperative breast MRI sequences are associated with the ALN metastasis status, where CE2 phase and the contrast enhancement kinetic features lead to the highest classification effect. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:1125-1132.
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