ArticleEuropean radiology2022
Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed, 61 citations in OpenAlex.
- 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
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- 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
- 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
- Integrating intratumoral and peritumoral radiomics with deep transfer learning from multiparametric MRI for preoperative prediction of HER2 status in breast cancer: a multicenter study.Frontiers in oncology · 2026Article
- Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer.International journal of molecular sciences · 2025Article
- Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.Breast cancer research : BCR · 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
- Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features.BMC medical imaging · 2025Article
- Multi-sequence MRI-based nomogram for prediction of human epidermal growth factor receptor 2 expression in breast cancer.Heliyon · 2025Article
- The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer.BMC medical imaging · 2025Article
- The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis.Frontiers in oncology · 2025Article
- Preoperative Imaging Assessment of Lymphovascular Invasion in Breast Cancer: Current Evidence, Technical Advances, and Future Directions.Breast cancer (Dove Medical Press) · 2025Review
- MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer.BMC medical imaging · 2024Article
- Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features.BMC medical imaging · 2024Article
- Development and validation of nomograms for predicting cardiovascular disease risk in patients with prediabetes and diabetes.Scientific reports · 2024Article
- Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.Scientific reports · 2024Article
- Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI.NPJ breast cancer · 2024Article
- A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer.Journal of imaging informatics in medicine · 2024Article
- Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer.Scientific reports · 2024Article
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7 authors at 2 institutions in 1 country.
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Abstract
objectiveTo develop a multiparametric MRI-based radiomics nomogram for predicting lymphovascular invasion (LVI) status and clinical outcomes in patients with breast invasive ductal carcinoma (IDC).
methodsA total of 160 patients with pathologically confirmed breast IDC (training cohort: n = 112; validation cohort: n = 48) who underwent preoperative breast MRI were included. Imaging features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted imaging (cT1WI) sequences. A four-step procedure was applied for feature selection and radiomics signature building. Univariate and multivariate logistic regression analyses were conducted to identify the features associated with LVI, which were then incorporated into the radiomics nomogram. The performance of the nomogram was evaluated by its discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the two radiomics models were used to estimate disease-free survival (DFS).
resultsThe fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. The proposed radiomics nomogram, incorporating the fusion radiomics signature and MRI-reported peritumoral edema, showed satisfactory capabilities of calibration and discrimination in both training and validation datasets, with AUCs of 0.919 (95% CI: 0.871-0.967) and 0.863 (95% CI: 0.726-0.999), respectively. The radiomics signature and nomogram-defined high-risk groups had a shorter DFS than those in the low-risk groups (both p < 0.05). Higher Rad-scores were independently associated with a worse DFS in the whole cohort (p < 0.05).
conclusionsThe proposed nomogram, incorporating multiparametric MRI-based radiomics signature and MRI-reported peritumoral edema, achieved a satisfactory preoperative prediction of LVI and clinical outcomes in IDC patients. KEY POINTS: • The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. • The proposed nomogram achieved a favorable prediction of LVI in IDC patients with AUCs of 0.919 and 0.863 in the training and validation datasets, respectively. • The radiomics model could classify patients into high- and low-risk groups with significant differences in DFS.
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