ArticleFrontiers in oncology2022
Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- Predicting lateral cervical lymph node involvement in papillary thyroid carcinoma patients: development of a nomogram model based on contrast-enhanced ultrasound images.Gland surgery · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI.Frontiers in oncology · 2026Article
- A deep learning model based on multiphase DCE-MRI for preoperative prediction of Ki-67 expression in breast cancer.Frontiers in oncology · 2026Article
- Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis.Medical & biological engineering & computing · 2025Article
- Improving the diagnostic performance of contrast-enhanced mammography through lesion conspicuity and enhancement quantification.European radiology · 2025Article
- Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.BMC medical imaging · 2025Article
- Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework.Radiology. Imaging cancer · 2025Article
- Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study.Insights into imaging · 2025Article
- Evaluating dynamic contrast-enhanced MRI for differentiating HER2-zero, HER2-low, and HER2-positive breast cancers in patients undergoing neoadjuvant chemotherapy.European journal of medical research · 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 value of intratumoral and peritumoral radiomics features based on multiparametric MRI for predicting molecular staging of breast cancer.Frontiers in oncology · 2025Article
- Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models.Frontiers in oncology · 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
- 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
- Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored.Insights into imaging · 2024Article
- Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2024Article
- Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study.Journal of personalized medicine · 2023Article
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9 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: The aim of this study is to investigate radiomics features extracted from the optimal peritumoral region and the intratumoral area on the early phase of dynamic contrast-enhanced MRI (DCE-MRI) for predicting molecular subtypes of invasive ductal breast carcinoma (IDBC). Methods: A total of 422 IDBC patients with immunohistochemical and fluorescence Results: A 6-mm peritumoral size was defined the optimal peritumoral region in classification tasks of hormone receptor (HR)-positive vs others, triple-negative breast cancer (TNBC) vs others, and HR-positive vs human epidermal growth factor receptor 2 (HER2)-enriched vs TNBC, and 8 mm was applied in HER2-enriched vs others. The combined clinical-radiological and radiomics models in three binary classification tasks (HR-positive vs others, HER2-enriched vs others, TNBC vs others) obtained optimal performance with AUCs of 0.838, 0.848, and 0.930 in the training cohort, respectively; 0.827, 0.813, and 0.879 in the internal test cohort, respectively; and 0.791, 0.707, and 0.852 in the external test cohort, respectively. Conclusion: Radiomics features in the intratumoral and peritumoral regions of IDBC on DCE-MRI had a potential to predict the HR-positive, HER2-enriched, and TNBC molecular subtypes preoperatively.
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