ArticleQuantitative imaging in medicine and surgery2021
A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 37 citations in OpenAlex.
- Development and validation of a nomogram to assess recurrence risk in young patients with breast cancer based on preoperative serum tumor markers.Gland surgery · 2026Article
- A nomogram based on conventional ultrasound and elastography for diagnosing BI-RADS category 3-5 lesions.BMC medical imaging · 2026Article
- Multimodal habitat radiomics based on automated breast volume scanning and conventional ultrasound for risk stratification of biopsy-confirmed BI-RADS 4A breast lesions.Frontiers in oncology · 2026Article
- Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study.JMIR medical informatics · 2025Article
- Over-detection and over-surveillance in breast screening: current status and the potential for artificial intelligence optimisation.Insights into imaging · 2025Review
- 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
- Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.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
- The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.Translational cancer research · 2024Article
- A bimodal nomogram: a non-invasive tool to assist breast radiologists in decision-making.European radiology · 2024Article
- Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features.BMC women's health · 2024Article
- Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.BMC medical imaging · 2023Article
- Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet.Diagnostics (Basel, Switzerland) · 2023Article
- A nomogram based on combining clinical features and contrast enhanced ultrasound is not able to identify Her-2 over-expressing cancer from other breast cancers.Frontiers in oncology · 2023Article
- Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions.Quantitative imaging in medicine and surgery · 2022Article
- Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study.Quantitative imaging in medicine and surgery · 2022Article
- Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study.Frontiers in oncology · 2022Article
- Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma.Frontiers in oncology · 2022Article
- A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS.Cancer management and research · 2021Article
- An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety.
methodsA prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation.
resultsThe independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR =4.588, P=0.004), US features [margin (OR =2.916, P=0.019), shape (irregular
conclusionsWe developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.