ArticleBMC medical imaging2021
Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research.Scientific reports · 2024Trial
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Transcriptome predictors of second breast events in ductal carcinoma in situ: a case‒control study including Black and White women.Breast cancer research : BCR · 2026Article
- Non-Invasive Breast Cancer Receptor Typing from Mammograms Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Development and multicenter external validation of an intratumoral and peritumoral ultrasound-based radiomics model for preoperative prediction of HER2 status in IHC 2 + breast cancer.European journal of medical research · 2025Article
- AI-Based Ultrasound Nomogram for Differentiating Invasive from Non-Invasive Breast Cancer Masses.Cancers · 2025Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
- Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.Frontiers in oncology · 2025Article
- Magnetic resonance imaging-based radiomics in predicting the expression of Ki-67, p53, and epidermal growth factor receptor in rectal cancer.Journal of gastrointestinal oncology · 2024Article
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
- Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.BMC medical imaging · 2024Article
- Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.The international journal of cardiovascular imaging · 2024Article
- Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinomaGland surgery · 2024Article
- Prognostic Value ofKorean journal of radiology · 2024Article
- A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study).Scientific reports · 2024Article
- Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer.Scientific reports · 2023Article
- A clinical radiomics nomogram preoperatively to predict ductal carcinoma in situ with microinvasion in women with biopsy-confirmed ductal carcinoma in situ: a preliminary study.BMC medical imaging · 2023Article
- Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions.Journal of translational medicine · 2023Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS.
methods116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve.
resultsThe investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively.
conclusionUltrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.
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