ArticleBMC medical imaging2025
Study on heterogeneity of vascularity and cellularity via multiparametric MRI habitat imaging in breast cancer.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MMBCFNet: multi modal hybrid deep learning framework for breast cancer detection using MRI, mammography, and ultrasound images.Journal of ultrasound · 2026Article
- Incremental Value of Synthetic MRI Radiomics in Predicting Neoadjuvant Response in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.Radiology. Imaging cancer · 2026Article
- Integrating tumor habitat heterogeneity with a hybrid deep learning architecture for ultrasound radiomics: a dual-center study on non-invasive prediction of PD-L1 expression in triple-negative breast cancer.Breast cancer research : BCR · 2026Article
- A multicenter study on preoperative WHO/ISUP grading of clear cell renal cell carcinoma using triphasic contrast-enhanced CT-based habitat imaging.BMC medical imaging · 2026Article
- Multiparametric MRI-based radiomics of whole-tumor and habitat regions for predicting HER2 status in young breast cancer: a two-center study.Frontiers in oncology · 2026Article
- MRI-based habitat radiomics for preoperative prediction of axillary pathological complete response in breast cancer after neoadjuvant therapy: a multicenter study.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThis study aimed to visually analyze the heterogeneity of vascularity and cellularity across different sub-regions of breast cancer using habitat imaging (HI) to predict human epidermal growth factor receptor 2 (HER2) expression and evaluate the effectiveness of neoadjuvant therapy (NAT) in breast cancer patients.
methodsA retrospective analysis was conducted on 76 patients diagnosed with breast cancer. Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) sequences were utilized to acquire MR images. Apparent diffusion coefficient (ADC), K
resultsThree distinct sub-regions within breast cancer lesions were identified through HI, characterized physiologically as: low vascularity-high cellularity (LV-HC), low vascularity-low cellularity (LV-LC), and high vascularity-low cellularity (HV-LC). Significant differences were observed in the proportions of these tumor sub-regions between HER2-positive and HER2-negative breast cancers. Additionally, HER2-low and HER2-zero breast cancers demonstrated statistical differences in the second sub-region (LV-LC). Furthermore, the proportion of the first sub-region (LV-HC) was negatively correlated with the efficacy of NAT in breast cancer patients.
conclusionsHabitat imaging can identify distinct sub-regions within breast cancer lesions, providing a noninvasive imaging biomarker for predicting HER2 expression levels and assessing the efficacy of NAT in breast cancer patients.
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