ArticleEuropean radiology2023
Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?Medical & biological engineering & computing · 2026Article
- Five-Year Breast Cancer Risk Prediction From Screening Breast Ultrasound Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
- Application of Artificial Intelligence in Breast Ultrasound Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population.Insights into imaging · 2026Article
- Direct deep learning analysis of three-dimensional automated breast ultrasound videos with reading mode optimization for breast cancer diagnosis.Ultrasonography (Seoul, Korea) · 2026Article
- Development and validation of an integrated model combining deep learning, radiomics, and clinical and breast ultrasound features for Breast Imaging Reporting and Data System 4A lesion malignancy classification.Quantitative imaging in medicine and surgery · 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
- Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.Breast cancer research : BCR · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of BI-RADS 4A lesions.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.La Radiologia medica · 2024Article
- Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.Heliyon · 2024Article
- Multiview deep learning networks based on automated breast volume scanner images for identifying breast cancer in BI-RADS 4.Frontiers in oncology · 2024Article
- Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study.EClinicalMedicine · 2023Article
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29 authors.
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Abstract
objectivesTo establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously.
methodsThis multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists.
resultsIn the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists.
conclusionsThe DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis. KEY POINTS: • The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.
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