ArticleFrontiers in oncology2022
Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 20 citations in OpenAlex.
- A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.Journal of imaging informatics in medicine · 2026Review
- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 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
- Article
- A Multimodal Ultrasound Radiomics Study for Discriminating Benign and Malignant Breast Lesions Classified as BI-RADS Category 4.International journal of general medicine · 2026Article
- Development and validation of a screening model for benign and malignant breast masses based on S-Detect and microvascular flow imaging.Gland surgery · 2025Article
- Ultrasound-based radiomics for the evaluation of breast cancer.Frontiers in oncology · 2025Review
- The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
- Construction of a nomogram prediction model for the pathological complete response after neoadjuvant chemotherapy in breast cancer: a study based on ultrasound and clinicopathological features.Frontiers in oncology · 2025Article
- A combined clinical-ultrasound radiomics model for differentiating benign and malignant BI-RADS category 4 breast masses.American journal of translational research · 2025Article
- Optimizing breast cancer diagnosis with photoacoustic imaging: An analysis of intratumoral and peritumoral radiomics.Photoacoustics · 2024Article
- A bimodal nomogram: a non-invasive tool to assist breast radiologists in decision-making.European radiology · 2024Article
- The role of systemic immune-inflammation index in predicting pathological complete response of breast cancer after neoadjuvant therapy and the establishment of related predictive model.Frontiers in oncology · 2024Article
- 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.Quantitative imaging in medicine and surgery · 2023Article
- A nomogram based on platelet-to-lymphocyte ratio for predicting pathological complete response of breast cancer after neoadjuvant chemotherapy.BMC cancer · 2023Article
- A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram.Frontiers in oncology · 2023Article
- Ultrasound Video-Based Radiomics Analysis for Differentiating Benign and Malignant Breast Lesions.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop nomograms for predicting breast malignancy in BI-RADS ultrasound (US) category 4 or 5 lesions based on radiomics features. Methods: Between January 2020 and January 2022, we prospectively collected and retrospectively analyzed the medical records of 496 patients pathologically proven breast lesions in our hospital. The data set was divided into model training group and validation testing group with a 75/25 split. Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram was developed for BI-RADS US category 4 or 5 lesions according to the results of multivariate regression analysis from the training group. Result: The AUCs of radiomics score consisting of 31 US features was 0.886. The AUC of the model constructed with radiomics score, patient age, lesion diameter identified by US and BI-RADS category involved was 0.956 (95% CI, 0.910-0.972) for the training group and 0.937 (95% CI, 0.893-0.965) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Conclusions: Both nomogram and radiomics score can be used as methods to assist radiologists and clinicians in predicting breast malignancy in BI-RADS US category 4 or 5 lesions.
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