ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Radiogenomic profiling to determine BRCA alteration status-a systematic review and meta-analysis.The British journal of radiology · 2025Pooled it
- DCE-MRI and Mammography integrated radiomic analysis in triple negative ductal invasive breast cancer patients. Comparison between BRCA and not BRCA mutated patients: preliminary results.European journal of radiology open · 2026Article
- Preliminary exploration of radiomic mammographic analysis in triple negative breast cancer related to BRCA profile.Scientific reports · 2026Article
- Review
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Fusing Radiomics and Ultrasonic Features: A Superior Nomogram for Diagnosing Cervical Tuberculous Lymphadenitis.Infection and drug resistance · 2026Article
- CT delta-radiomics predicts the risks of blood transfusion and massive bleeding during spinal tumor surgery.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Predicting Pathogenic Variants of Breast Cancer Using Ultrasound-Derived Machine Learning Models.Cancers · 2025Article
- Ultrasound-based radiogenomics: status, applications, and future direction.Ultrasonography (Seoul, Korea) · 2025Article
- Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics.Frontiers in oncology · 2025Article
- An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in TTechnology in cancer research & treatmentArticle
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIdentifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to develop a nomogram incorporating ultrasound radiomic features and clinicopathological factors to predict gBRCA mutations in patients with BC. MATERIALS AND
methodsIn this retrospective study, 497 women with BC who underwent gBRCA genetic testing from March 2013 to May 2022 were included, including 348 for training (84 with and 264 without a gBRCA mutation) and 149 for validation(36 patients with and 113 without a gBRCA mutation). Factors associated with gBRCA mutations were identified to establish a clinicopathological model. Radiomics features were extracted from the intratumoral and peritumoral regions (3 mm and 5 mm) of each image. The least absolute shrinkage and selection operator regression algorithm was used to select the features and logistic regression analysis was used to construct three imaging models. Finally, a nomogram that combined clinicopathological and radiomics features was developed. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness.
resultsAge at diagnosis, family history of BC, personal history of other BRCA-related cancers, and human epidermal growth factor receptor 2 status were independent predictors of the clinicopathological model. The AUC of the imaging radiomics model combining intratumoral and peritumoral 3 mm areas in the validation set was 0.783 (95% confidence interval [CI]: 0.702-0.862), which showed the best performance among three imaging models. The nomogram yielded better performance than the clinicopathological model in validation sets (AUC: 0.824 [0.755-0.894] versus 0.659 [0.563-0.755], p = 0.007).
conclusionThe nomogram based on ultrasound images and clinicopathological factors performs well in predicting gBRCA mutations in BC patients and may help to improve clinical decisions about genetic testing.
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