Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization
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.
Authors
- Maria Carmen De Santis (ORCID: https://orcid.org/0000-0003-3131-3969)
- Miria Lafranceschina (ORCID: https://orcid.org/0000-0002-1170-3698)
- C. Tenconi
- Charlotte Marguerite Lucille Trombadori (ORCID: https://orcid.org/0000-0002-1238-9688)
- Paolo Belli (ORCID: https://orcid.org/0000-0001-7979-2466)
- Valeria De Soccio (ORCID: https://orcid.org/0000-0003-1309-028X)
- Sergio Venanzio Setola (ORCID: https://orcid.org/0000-0002-9922-4033)
- Vincenza Granata (ORCID: https://orcid.org/0000-0002-6601-3221)
- Laura Greco (ORCID: https://orcid.org/0000-0002-3914-8614)
- Antonello Vidiri
- Annarita Fanizzi (ORCID: https://orcid.org/0000-0002-2729-9896)
- Francesca Ferranti (ORCID: https://orcid.org/0000-0002-4648-4069)
- Antonella Petrillo (ORCID: https://orcid.org/0000-0003-2465-5370)
- Gerardo Ferrara (ORCID: https://orcid.org/0000-0003-0727-4015)
- Enrico Cassano (ORCID: https://orcid.org/0000-0002-3437-8939)
- Daniele La Forgia (ORCID: https://orcid.org/0000-0002-3902-4523)
- Giovanna Trecate (ORCID: https://orcid.org/0000-0002-2666-0627)
- Paolo Vallone
- Roberta Fusco (ORCID: https://orcid.org/0000-0002-0469-9969)
- Raffaella Massafra (ORCID: https://orcid.org/0000-0001-8588-261X)
- Valeria Dominelli
- Teresa Petrosino
- Luca Boldrini (ORCID: https://orcid.org/0000-0002-5631-1575)
- Davide Pupo (ORCID: https://orcid.org/0000-0002-7967-0853)
- Francesca Botta (ORCID: https://orcid.org/0000-0002-3693-8899)
- Mauro Mattace Raso (ORCID: https://orcid.org/0000-0002-7254-5013)
- Maria Assunta Daniela Iasevoli
Institutions
- Agostino Gemelli University Polyclinic (IT)
- Istituto Tumori Bari (IT)
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- European Institute of Oncology (IT)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-09-02
- DOI
- https://doi.org/10.3390/bioengineering12090952
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Ministero della Salute
- Alleanza Contro il Cancro
- National Cancer Institute