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References from Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles. Local targets link to admitted publications; unresolved targets remain external evidence.
Factors associated with the occurrence of breast nodules in perimenopausal women: a cross‐sectional study in China
10.1097/md.0000000000049036 · 2026 · External reference
Application of ultrasound radiomics in differentiating benign from malignant breast nodules in women with post‐silicone breast augmentation
10.3390/curroncol32010029 · 2025 · External reference
A nomogram for diagnosis of BI‐RADS 4 breast nodules based on three‐dimensional volume ultrasound
10.1186/s12880-025-01580-w · 2025 · External reference
Application value of deep learning‐based AI model in the classification of breast nodules
2025 · External reference
Ultrasound radiomics‐based logistic regression model to differentiate between benign and malignant breast nodules
2022 · External reference
Feasibility of using AI to auto‐catch responsible frames in ultrasound screening for breast cancer diagnosis
10.1016/j.isci.2022.105692 · 2023 · External reference
An AI model of sonographer's evaluation + S‐Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses
10.3389/fonc.2022.1022441 · 2022 · External reference
The role of combined ultrasonic BI‐RADS and ultrasound elastography in discriminating between benign and malignant breast nodules
10.1097/md.0000000000047117 · 2026 · External reference
Diagnostic value of shear wave elastography combined with super microvascular imaging for BI‐RADS 3–5 nodules
10.3389/fonc.2023.1192630 · 2023 · External reference
The application value of ultrasound imaging in the differential diagnosis of benign and malignant breast nodules of BI‐RADS 3 and above
10.26689/par.v8i2.6405 · 2024 · External reference
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
10.21037/qims-22-1091 · 2023 · External reference
Detection and BI‐RADS classification of breast nodules in urban women—China, 2021
10.46234/ccdcw2025.056 · 2025 · External reference
Optimizing S‐detect classification accuracy for BI‐RADS 4 breast nodules using multimodal ultrasound parameters
10.21037/qims-2025-1092 · 2026 · External reference
Can new ultrasound imaging techniques improve breast lesion characterization? Prospective comparison between ultrasound BI‐RADS and semi‐automatic software “SmartBreast”, strain elastography, and shear wave elastography
10.3390/app13116764 · 2023 · External reference
Diagnostic performance of ultrasound S‐Detect technology in evaluating BI‐RADS‐4 breast nodules ≤ 20 mm and >20 mm
10.1186/s12885-025-14760-2 · 2025 · External reference
Development of a nomogram‐based model combining intra‐ and peritumoral ultrasound radiomics with clinical features for differentiating benign from malignant in breast imaging reporting and data system category 3–5 nodules
10.21037/qims-23-283 · 2023 · External reference
Development and validation of an intratumoral‐peritumoral deep transfer learning fusion model for differentiating BI‐RADS 3–4 breast nodules
10.21037/gs-24-457 · 2025 · External reference
Artificial intelligence breast ultrasound and handheld ultrasound in the BI‐RADS categorization of breast lesions: a pilot head to head comparison study in screening program
10.3389/fpubh.2022.1098639 · 2023 · External reference
Ultrasonographic characteristics of BI‐RADS category 4 breast lesions in males
10.11152/mu-4510 · 2025 · External reference
Integrating the incident angle of main vessel of breast nodule with BI‐RADS can improve the efficacy of breast malignancy evaluation
10.1038/s41598-024-71567-0 · 2024 · External reference
Differential diagnosis model of benign and malignant breast BI‐RADS category 4 nodules based on serum SP70 and conventional laboratory indicators
2022 · External reference
Maximizing diagnostic precision: evaluating the combined Yokohama and BI‐RADS scoring system for breast lesions
2025 · External reference
Automated diagnosis of multi‐plane breast ultrasonography images using deep neural networks
10.1016/j.neucom.2020.04.123 · 2020 · External reference
