{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T12:22:17Z","timestamp":1784895737423,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health","award":["RCR-2021-23671213"],"award-info":[{"award-number":["RCR-2021-23671213"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2\u2212). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (\u03b1 = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice.<\/jats:p>","DOI":"10.3390\/bioengineering12090952","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T16:05:22Z","timestamp":1756829122000},"page":"952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-9969","authenticated-orcid":false,"given":"Roberta","family":"Fusco","sequence":"first","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6601-3221","authenticated-orcid":false,"given":"Vincenza","family":"Granata","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Petrosino","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Vallone","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Assunta Daniela","family":"Iasevoli","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Mattace Raso","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio Venanzio","family":"Setola","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7967-0853","authenticated-orcid":false,"given":"Davide","family":"Pupo","sequence":"additional","affiliation":[{"name":"Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerardo","family":"Ferrara","sequence":"additional","affiliation":[{"name":"Pathology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2729-9896","authenticated-orcid":false,"given":"Annarita","family":"Fanizzi","sequence":"additional","affiliation":[{"name":"Direzione Scientifica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raffaella","family":"Massafra","sequence":"additional","affiliation":[{"name":"SSD Fisica Sanitaria, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miria","family":"Lafranceschina","sequence":"additional","affiliation":[{"name":"Struttura Semplice Dipartimentale di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3902-4523","authenticated-orcid":false,"given":"Daniele","family":"La Forgia","sequence":"additional","affiliation":[{"name":"Struttura Semplice Dipartimentale di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Greco","sequence":"additional","affiliation":[{"name":"Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, 00144 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca Romana","family":"Ferranti","sequence":"additional","affiliation":[{"name":"Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, 00144 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria","family":"De Soccio","sequence":"additional","affiliation":[{"name":"Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, 00144 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6203-4642","authenticated-orcid":false,"given":"Antonello","family":"Vidiri","sequence":"additional","affiliation":[{"name":"Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, 00144 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3693-8899","authenticated-orcid":false,"given":"Francesca","family":"Botta","sequence":"additional","affiliation":[{"name":"Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria","family":"Dominelli","sequence":"additional","affiliation":[{"name":"Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3437-8939","authenticated-orcid":false,"given":"Enrico","family":"Cassano","sequence":"additional","affiliation":[{"name":"Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1238-9688","authenticated-orcid":false,"given":"Charlotte Marguerite Lucille","family":"Trombadori","sequence":"additional","affiliation":[{"name":"Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. 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