ArticleInsights into imaging2022
Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Deep Learning Algorithms Versus Radiologists in Digital Breast Tomosynthesis for Breast Cancer Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population.Insights into imaging · 2026Article
- Article
- A Deep Learning Model Integrating Ultrasound Images and Multidimensional Clinical Information for Differentiating Benign and Malignant Non-Mass Lesions.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Multi-level semi-supervised GAN enables accurate segmentation of breast ultrasound images.Scientific reports · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- Direct deep learning analysis of three-dimensional automated breast ultrasound videos with reading mode optimization for breast cancer diagnosis.Ultrasonography (Seoul, Korea) · 2026Article
- Explainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images.Scientific reports · 2025Article
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- Predicting Very Early-Stage Breast Cancer in BI-RADS 3 Lesions of Large Population with Deep Learning.Journal of imaging · 2025Article
- Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Development and validation of an intratumoral-peritumoral deep transfer learning fusion model for differentiating BI-RADS 3-4 breast nodules.Gland surgery · 2025Article
- Breast Cancer Detection on Dual-View Sonography via Data-Centric Deep Learning.IEEE open journal of engineering in medicine and biology · 2025Article
- The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024.Diagnostics (Basel, Switzerland) · 2024Review
- Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review.NPJ precision oncology · 2024Review
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Authors and funding
29 authors at 19 institutions in 1 country.
Funding
Abstract
backgroundStudies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists.
methodsA total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model's ability to assist the radiologists using two different methods.
resultsThe model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities.
conclusionsThe DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.
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