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
Radiomic analysis of T2-weighted MRI, particularly through deep learning models like neural networks, demonstrated the highest overall diagnostic performance for predicting metastatic ALNs. In contrast, arterial-phase T1-weighted features showed better results in univariate analysis. These findings support the integration of radiomic features, especially from T2-weighted sequences, into multivariate models to enhance noninvasive preoperative assessment.
Authors
- C Siani (ORCID: https://orcid.org/0000-0001-7584-2569)
- Sergio Venanzio Setola (ORCID: https://orcid.org/0000-0002-9922-4033)
- Vincenza Granata (ORCID: https://orcid.org/0000-0002-6601-3221)
- Antonella Petrillo (ORCID: https://orcid.org/0000-0003-2465-5370)
- Paolo Pariante
- Paolo Vallone
- Gerardo Ferrara
- Saverio Simonelli
- Roberta Fusco (ORCID: https://orcid.org/0000-0002-0469-9969)
- Paola Gargiulo (ORCID: https://orcid.org/0000-0003-4622-1725)
- Davide Pupo (ORCID: https://orcid.org/0000-0002-7967-0853)
- Mauro Mattace Raso (ORCID: https://orcid.org/0000-0002-7254-5013)
- Igino Simonetti
- Maria Assunta Daniela Iasevoli
- Filippo Tovecci
- Francesca Maio
- Raimondo Di Giacomo
- G. Giannotti
Institutions
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
Publication Details
- Journal
- La radiologia medica
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1007/s11547-025-02090-z
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute
- National Cancer Institute