ArticleBMC cancer2020
Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ESR Essentials: artificial intelligence in breast imaging-practice recommendations by the European Society of Breast Imaging.European radiology · 2026Guideline
- Development and validation of radiomics models for the diagnoses of breast lesions with calcification on ultrasound.Quantitative imaging in medicine and surgery · 2026Article
- 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
- 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
- Diagnostic value of ultrasound radiomic features in differentiating benign and malignant breast lesions.Journal of ultrasound · 2025Article
- AI-Based Ultrasound Nomogram for Differentiating Invasive from Non-Invasive Breast Cancer Masses.Cancers · 2025Article
- Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.Breast cancer research and treatment · 2025Article
- Accuracy of breast ultrasound image analysis software in feature analysis: a comparative study with sonographers.Scientific reports · 2024Article
- Novel study on the prediction of BI-RADS 4A positive lesions in mammography using deep learning technology and clinical factors.Quantitative imaging in medicine and surgery · 2024Article
- Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.Frontiers in oncology · 2024Article
- 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
- Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence diagnosis based on breast ultrasound imaging.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Application of CT Imaging in Differential Diagnosis and Nursing of Endocrine Tumors.Contrast media & molecular imaging · 2022Article
- Ultrasound radiomics in personalized breast management: Current status and future prospects.Frontiers in oncology · 2022Review
- The effect of the use of the Gail model on breast cancer diagnosis in BIRADs 4a cases.Turkish journal of surgery · 2021Article
- AI-enhanced breast imaging: Where are we and where are we heading?European journal of radiology · 2021Review
- Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data.Quantitative imaging in medicine and surgery · 2021Article
- Application of artificial intelligence in predicting malignancy risk in breast masses on ultrasound.Radiologia brasileiraArticle
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19 authors.
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Abstract
backgroundThe classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.
methodsA total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.
resultsSignificant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.
conclusionsCompared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.
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