Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy
The explainable radiomics model we propose can better predict the treatment response of lung metastasis after SBRT and provide further guidance for clinical practice.
Authors
- Savino Cilla (ORCID: https://orcid.org/0000-0001-6711-350X)
- Donato Pezzulla (ORCID: https://orcid.org/0000-0001-6676-1169)
- Francesco Deodato (ORCID: https://orcid.org/0000-0003-1276-5070)
- Milly Buwenge (ORCID: https://orcid.org/0000-0002-0779-1875)
- Erika Galietta (ORCID: https://orcid.org/0000-0001-5202-3472)
- A.G. Morganti (ORCID: https://orcid.org/0000-0001-9793-3410)
- Elisabetta Lepre
- Carmela Romano (ORCID: https://orcid.org/0000-0003-1796-9873)
- Gabriella Macchia (ORCID: https://orcid.org/0000-0002-0529-201X)
- Costanza Maria Donati
Institutions
- Università Cattolica del Sacro Cuore (IT)
- Physicians Committee for Responsible Medicine (US)
- Agostino Gemelli University Polyclinic (IT)
- Azienda USL di Bologna (IT)
- University of Bologna (IT)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2025-10-10
- DOI
- https://doi.org/10.1093/bjr/tqaf043
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36