ArticleScientific reports2015
Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision.
Article in Scientific reports, 2015. 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.
- Article
- TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- A nomogram to predict disease-free survival in patients with residual triple-negative breast cancer after neoadjuvant chemotherapy based on clinicopathological and sonographic features.Translational cancer research · 2026Article
- Nomogram combining clinical and ultrasonographic features for differentiating granulomatous lobular mastitis from invasive lobular carcinoma.Frontiers in oncology · 2026Article
- Correlation between ultrasonography and elastography parameters and molecular subtypes of breast cancer in young women.Annals of medicine · 2025Article
- Relation knowledge distillation 3D-ResNet-based deep learning for breast cancer molecular subtypes prediction on ultrasound videos: a multicenter study.British journal of cancer · 2025Article
- Differentiation of triple-negative breast cancer and benign breast lesions using multiparametric ultrasonography.Journal of ultrasonography · 2025Article
- Prediction model for assessing HER2 status patient with invasive ductal carcinoma based on clinical parameters and ultrasound features: a dual-center study.BMC women's health · 2025Article
- Prediction models of breast cancer molecular subtypes based on multimodal ultrasound and clinical features.BMC cancer · 2025Article
- Relationship between the ultrasound features of different molecular subtypes of breast cancer and positive PD-1/PD-L1 expression.The Journal of international medical research · 2025Observational
- Ultrasound-based DenseNet201 outperforms breast imaging reporting and data system and ResNet50 in predicting HER2 status in invasive breast cancer.American journal of translational research · 2025Article
- Ultrasonography: an aid in molecular subtyping of breast carcinoma.Journal of ultrasonography · 2025Article
- Correlation of preoperative sonographic staging and postoperative histopathologic staging in patients with invasive breast cancer.Archives of gynecology and obstetrics · 2024Article
- A hybrid features fusion-based framework for classification of breast micronodules using ultrasonography.BMC medical imaging · 2024Article
- Nomograms for predicting recurrence of HER2-positive breast cancer with different HR status based on ultrasound and clinicopathological characteristics.Cancer medicine · 2024Article
- The added value of ultrasound imaging biomarkers to clinicopathological factors for the prediction of high-risk Oncotype DX recurrence scores in patients with breast cancer.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images.Medical & biological engineering & computing · 2023Article
- Association between ultrasound BI-RADS signs and molecular typing of invasive breast cancer.Frontiers in oncology · 2023Article
- Imaging features of breast cancer subtypes on contrast enhanced ultrasound: a feasibility study.Ecancermedicalscience · 2023Article
- Towards Refining Breast Cancer Diagnosis on Ultrasound.Journal of ultrasonography · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is molecularly heterogeneous and categorized into four molecular subtypes: Luminal-A, Luminal-B, HER2-amplified and Triple-negative. In this study, we aimed to apply an ensemble decision approach to identify the ultrasound and clinical features related to the molecular subtypes. We collected ultrasound and clinical features from 1,000 breast cancer patients and performed immunohistochemistry on these samples. We used the ensemble decision approach to select unique features and to construct decision models. The decision model for Luminal-A subtype was constructed based on the presence of an echogenic halo and post-acoustic shadowing or indifference. The decision model for Luminal-B subtype was constructed based on the absence of an echogenic halo and vascularity. The decision model for HER2-amplified subtype was constructed based on the presence of post-acoustic enhancement, calcification, vascularity and advanced age. The model for Triple-negative subtype followed two rules. One was based on irregular shape, lobulate margin contour, the absence of calcification and hypovascularity, whereas the other was based on oval shape, hypovascularity and micro-lobulate margin contour. The accuracies of the models were 83.8%, 77.4%, 87.9% and 92.7%, respectively. We identified specific features of each molecular subtype and expanded the scope of ultrasound for making diagnoses using these decision models.
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