Observational studyBMJ open2020
Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study.
Observational study in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Exploring the Application of Contrast-Enhanced Ultrasound in ACR BI-RADS 4A Lesions (≤ 2 cm): A Prospective Multicenter Study in China.The breast journal · 2026Article
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Integration of AI lesion classification, age, and BI-RADS assessment to reduce benign biopsies on breast ultrasound.European radiology · 2025Article
- Value of deep learning model for predicting Breast Imaging Reporting and Data System 3 and 4A lesions on mammography.Quantitative imaging in medicine and surgery · 2025Article
- Evaluation of BI-RADS 3 Ultrasound Findings: the Frequency and Incidence of Malignant Lesions, and Tumor Size.Acta medica academica · 2025Article
- A combined clinical-ultrasound radiomics model for differentiating benign and malignant BI-RADS category 4 breast masses.American journal of translational research · 2025Article
- Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study.World journal of surgery · 2023Article
- Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer.Heliyon · 2023Article
- Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.BMC medical imaging · 2023Article
- Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3-5 nodule classification among radiologists: a multiple center study.Quantitative imaging in medicine and surgery · 2023Article
- The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis.Gland surgery · 2022Article
- Improving breast cancer diagnostics with deep learning for MRI.Science translational medicine · 2022Article
- A deep learning-based diagnostic pattern for ultrasound breast imaging: can it reduce unnecessary biopsy?Gland surgery · 2022Article
- The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.European radiology · 2022Article
- A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses.Diagnostics (Basel, Switzerland) · 2022Article
- Can Pre-biopsy Second-Look Breast Ultrasound Affect Clinical Management? Experience From a Single Tertiary Hospital.Frontiers in oncology · 2022Article
- Evaluating breast ultrasound S-detect image analysis for small focal breast lesions.Frontiers in oncology · 2022Article
- Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams.Nature communications · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm.
methodsThe study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents.
participantsA total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions.
resultsS-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%-48.67%, AUC: 0.62-0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%-76.11%, AUC: 0.71-0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643).
conclusionsWith the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.
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