ArticleJournal of magnetic resonance imaging : JMRI2024
Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer.Cancer medicine · 2024Trial
- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 2026Article
- MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.Insights into imaging · 2026Article
- Use of contrast-enhanced mammography for preoperative prediction of lymphovascular invasion status in invasive breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Mapping the Intratumoral and Peritumoral Microenvironment: Multilayered Shell ADC Analysis and Its Association with Multiparametric Biomarkers in Invasive Breast Cancer.Tomography (Ann Arbor, Mich.) · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.BMC medical imaging · 2026Article
- Machine Learning Applied to CT Radiomics Identifies Symptomatic Carotid Plaques.Current medical imaging · 2026Article
- Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer.PloS one · 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 radiomics predicts different HER2 expression in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A non-invasive MRI-based multimodal fusion deep learning model (MF-DLM) for predicting overall survival in bladder cancer: a multicentre retrospective study.EClinicalMedicine · 2025Article
- Noninvasive prediction of lymphovascular invasion in rectal cancer without lymph node metastasis using a SHAP-interpretable combined model integrating MRI radiomics features and clinical immune-inflammatory biomarkers: a bicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Prediction of lymphovascular invasion in invasive breast cancer via intratumoral and peritumoral multiparametric magnetic resonance imaging machine learning-based radiomics with Shapley additive explanations interpretability analysis.Quantitative imaging in medicine and surgery · 2025Article
- Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer.NPJ precision oncology · 2025Article
- MRI radiomics for diagnosing small BI-RADS 4 breast lesions: an interpretable model.Quantitative imaging in medicine and surgery · 2025Article
- Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2025Article
- Preoperative CT-based Intratumoral and Peritumoral Radiomics Prediction for Vasculogenic Mimicry in Lung Adenocarcinoma.Current medical imaging · 2025Article
- Preoperative Imaging Assessment of Lymphovascular Invasion in Breast Cancer: Current Evidence, Technical Advances, and Future Directions.Breast cancer (Dove Medical Press) · 2025Review
- Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.Breast cancer research : BCR · 2024Article
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9 authors.
Funding
Abstract
backgroundRadiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. PURPOSE: To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. STUDY TYPE: Retrospective. POPULATION: Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). ASSESSMENT: Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC. STATISTICAL TESTS: Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram.
resultsA total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. DATA
conclusionThe constructed preoperative nomogram might effectively assess LVI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.
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