ArticleEuropean radiology2023
Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- 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
- APASA: adaptive selection of informative peritumoral regions for improved automated cancer lesion analysis.Scientific reports · 2026Article
- Use of contrast-enhanced mammography for preoperative prediction of lymphovascular invasion status in invasive breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study.Hepatology international · 2025Article
- Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models -- dual-center study.BMC cancer · 2025Article
- Development and validation of a prediction model for lymph node metastasis in thyroid cancer: integrating deep learning and radiomics features from intra- and peri-tumoral regions.Gland surgery · 2025Article
- Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.BMC medical imaging · 2025Article
- Optimizing Deep Learning Models for Luminal and Nonluminal Breast Cancer Classification Using Multidimensional ROI in DCE-MRI-A Multicenter Study.Cancer medicine · 2025Article
- Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features.BMC medical imaging · 2025Article
- Predicting lymph node metastasis in papillary thyroid carcinoma: radiomics using two types of ultrasound elastography.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Intra-tumor and peritumoral radiomics and deep learning based on ultrasound for differentiating fibroadenoma and phyllodes tumor: a multicenter study.Frontiers in oncology · 2025Article
- Factors Influencing Background Parenchymal Enhancement in Breast Contrast-Enhanced Mammography: A Retrospective Study.International journal of women's health · 2025Article
- Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2025Article
- Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging.Frontiers in oncology · 2025Article
- Enhancing preoperative HER2 status classification of invasive breast cancers using machine learning models based on clinicopathological and MRI features: a multicenter study.Frontiers in cell and developmental biology · 2025Article
- CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics.Journal of ovarian research · 2024Article
- Incidence rate of occult lymph node metastasis in clinical TRespiratory research · 2024Article
- Contrast-enhanced mammography: better with AI?European radiology · 2024Article
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14 authors.
Funding
Abstract
objectiveTo construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography.
methodsA total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists.
resultsThe nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram.
conclusionThe nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities. KEY POINTS: • Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions. • The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.
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