ArticleLa Radiologia medica2024
A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.
Article in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Deep Learning on Tumor Habitat Sub-regions vs. Whole-Tumor ROI: A Comparative Study of Fusion Strategies for Breast Cancer Molecular Subtype Prediction.Journal of imaging informatics in medicine · 2026Article
- Interpretable Whole-Breast Radiomic Biomarkers for Exploratory Assessment of HER2 + Breast Cancer in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- Clinically Robust Deep Learning for Contrast-Enhanced Mammography: Multicenter Evaluation Across Convolutional Neural Network Architectures.Bioengineering (Basel, Switzerland) · 2026Article
- Engineering the Image Representation for Deep Learning in Contrast-Enhanced Mammography: A Systematic Analysis of Preprocessing and Anatomical Masking.Bioengineering (Basel, Switzerland) · 2026Article
- Integrating deep learning and radiomics for precise identification of luminal A/B breast cancer subtypes on dynamic contrast-enhanced MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 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
- Assessing the multi-software robustness of radiomic biomarkers: a three-tool evaluation.Frontiers in oncology · 2026Article
- Radiomics integrated with machine and deep learning analysis of T2-weighted and arterial-phase T1-weighted Magnetic Resonance Imaging for non-invasive detection of metastatic axillary lymph nodes in breast cancer.La Radiologia medica · 2025Article
- Cross-Software Radiomic Feature Robustness Assessed by Hierarchical Clustering and Composite Index Analysis: A Multi-Cancer Study on Colorectal and Liver Lesions.Bioengineering (Basel, Switzerland) · 2025Article
- Review
- Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization.Bioengineering (Basel, Switzerland) · 2025Article
- Morphodynamic Features of Contrast-Enhanced Mammography and Their Correlation with Breast Cancer Histopathology.Journal of imaging · 2025Article
- Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer.Frontiers in oncology · 2025Review
- Radiomics in breast cancer: Current advances and future directions.Cell reports. Medicine · 2024Review
- An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer.
methodsFrom four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered.
resultsThe univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set.
conclusionsThe results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.
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Registered trials
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