ArticleFrontiers in oncology2020
Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for Identifying Benign and Malignant Breast Lesions of Sub-1 cm.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Radiomics-based mammographic abnormality identification via radiologist annotations.BJR artificial intelligence · 2026Article
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- Improving the diagnostic performance of contrast-enhanced mammography through lesion conspicuity and enhancement quantification.European radiology · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Hype or hope? A review of challenges in balancing tumor control and treatment toxicity in breast cancer from the perspective of the radiation oncologist.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Review
- Review
- An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study.Cancer medicine · 2023Article
- AI-Based Cancer Detection Model for Contrast-Enhanced Mammography.Bioengineering (Basel, Switzerland) · 2023Article
- Radiomic analysis in contrast-enhanced mammography using a multivendor data set: accuracy of models according to segmentation techniques.The British journal of radiology · 2023Article
- Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).Diagnostics (Basel, Switzerland) · 2023Article
- Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study.British journal of cancer · 2023Article
- A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions.Frontiers in oncology · 2023Article
- Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.Journal of X-ray science and technology · 2023Article
- Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer.Frontiers in oncology · 2023Article
- Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions.Methods and protocols · 2022Review
- Identifying factors that may influence the classification performance of radiomics models using contrast-enhanced mammography (CEM) images.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography.European radiology · 2022Article
- Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions.European radiology · 2022Article
- Long non-coding RNA CASC7 suppresses malignant behaviors of breast cancer by regulating miR-21-5p/FASLG axis.Bioengineered · 2021Article
- Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study.Quantitative imaging in medicine and surgery · 2021Article
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Abstract
objectivesTo develop a radiomics nomogram that incorporates contrast-enhanced spectral mammography (CESM)-based radiomics features and clinico-radiological variables for identifying benign and malignant breast lesions of sub-1 cm.
methodsThis retrospective study included 139 patients with the diameter of sub-1 cm on cranial caudal (CC) position of recombined images. Radiomics features were extracted from low-energy and recombined images on CC position. The variance threshold, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select optimal predictive features. Radiomics signature (Rad-score) was calculated by a linear combination of selected features. The independent predictive factors were identified by ANOVA and multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability of lesions. The performance and clinical utility of the nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
resultsNineteen radiomics features were selected to calculate Rad-score. Breast imaging reporting and data system (BI-RADS) category and age were identified as predictive factors. The radiomics nomogram combined with Rad-score, BI-RADS category, and age showed better performance (area under curves [AUC]: 0.940, 95% confidence interval [CI]: 0.804-0.992) than Rad-score (AUC: 0.868, 95% CI: 0.711-0.958) and clinico-radiological model (AUC: 0.864, 95% CI: 0.706-0.956) in the validation cohort. The calibration curve and DCA showed that the radiomics nomogram had good consistency and clinical utility.
conclusionsThe radiomics nomogram incorporated with CESM-based radiomics features, BI-RADS category and age could identify benign and malignant breast lesions of sub-1 cm.
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