ArticleEuropean radiology2022
The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03887598 (A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography), which is not on this map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
Who cites it
22 citing papers in PubMed.
- Automated breast ultrasound (ABUS) meets artificial intelligence: a new accurate classification tool for BIRADS 3-4 breast lesions.Journal of ultrasound · 2026Article
- Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?Medical & biological engineering & computing · 2026Article
- Development and validation of an interpretable ultrasound radiomics model for benign and malignant classification of breast lesions: a multicenter large-sample study.Insights into imaging · 2026Article
- Application of Artificial Intelligence in Breast Ultrasound Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Integration of AI lesion classification, age, and BI-RADS assessment to reduce benign biopsies on breast ultrasound.European radiology · 2025Article
- Diagnostic performance of ultrasound S-Detect technology in evaluating BI-RADS-4 breast nodules ≤ 20 mm and > 20 mm.BMC cancer · 2025Article
- Letter to the Editor regarding study design for diagnostic performance of S-detect in differentiating breast masses.The British journal of radiology · 2025Article
- Random forest with preoperative core biopsy categories: a novel method for refining ultrasonic Breast Imaging Reporting and Data System evaluation.Quantitative imaging in medicine and surgery · 2025Article
- Development and validation of a screening model for benign and malignant breast masses based on S-Detect and microvascular flow imaging.Gland surgery · 2025Article
- Bootstrapping BI-RADS classification using large language models and transformers in breast magnetic resonance imaging reports.Visual computing for industry, biomedicine, and art · 2025Article
- Comparative Study of AI Modes in Ultrasound Diagnosis of Breast Lesions.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.Frontiers in endocrinology · 2025Article
- Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study.The British journal of radiology · 2024Article
- Errors in Radiology: A Standard Review.Journal of clinical medicine · 2024Review
- 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
- Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review.NPJ precision oncology · 2024Review
- Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study.World journal of surgery · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
objectivesTo evaluate the diagnostic value of computer-aided diagnosis (CAD) software on ultrasound in distinguishing benign and malignant breast masses and avoiding unnecessary biopsy.
methodsThis prospective, multicenter study included patients who were scheduled for pathological diagnosis of breast masses between April 2019 and November 2020. Ultrasound images, videos, CAD analysis, and BI-RADS were obtained. The AUC, accuracy, sensitivity, specificity, PPV, and NPV were calculated and compared with radiologists.
resultsOverall, 901 breast masses in 901 patients were enrolled in this study. The accuracy, sensitivity, specificity, PPV and NPV of CAD software were 89.6%, 94.2%, 87.0%, 80.4%, and 96.3, respectively, in the long-axis section; 89.0%, 91.4%, 87.7%, 80.8%, and 94.7%, respectively, in the short-axis section. With BI-RADS 4a as the cut-off value, CAD software has a higher AUC (0.906 vs 0.734 vs 0.696, all p < 0.001) than both experienced and less experienced radiologists. With BI-RADS 4b as the cut-off value, CAD software showed better AUC than less experienced radiologists (0.906 vs 0.874, p < 0.001), but not superior to experienced radiologists (0.906 vs 0.883, p = 0.057). After the application of CAD software, the unnecessary biopsy rate of BI-RADS categories 4 and 5 was significantly decreased (33.0% vs 11.9%, 37.8% vs 14.5%), and the malignant rate of biopsy in category 4a was significantly increased (11.6% vs 40.7%, 7.4% vs 34.9%, all p < 0.001).
conclusionsCAD software on ultrasound can be used as an effective auxiliary diagnostic tool for differential diagnosis of benign and malignant breast masses and reducing unnecessary biopsy. CLINICAL
trial registrationClinicalTrials.gov (NCT03887598) KEY POINTS: • Prospective multicenter study showed that computer-aided diagnosis software provides greater diagnostic confidence for differentiating benign and malignant breast masses. • Computer-aided diagnosis software can help radiologists reduce unnecessary biopsy. • The management of patients with breast masses becomes more appropriate.
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