Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia
Our study provides new insights into frontotemporal network changes associated with bvFTD, highlighting specific associations between network measures and clinical/biological features. Radiomics feature similarity analysis could represent a useful approach for characterizing brain changes in patients with frontotemporal dementia.
Authors
- Stefano Zoccolella (ORCID: https://orcid.org/0000-0003-4037-7630)
- Alessia Giugno (ORCID: https://orcid.org/0000-0002-9366-1582)
- Giammarco Milella (ORCID: https://orcid.org/0000-0002-7612-382X)
- Salvatore Nigro (ORCID: https://orcid.org/0000-0001-7566-7983)
- Daniele Urso (ORCID: https://orcid.org/0000-0002-4503-0110)
- Benedetta Tafuri (ORCID: https://orcid.org/0000-0002-2931-6039)
- Marco Filardi (ORCID: https://orcid.org/0000-0001-9702-8314)
- Giancarlo Logroscino (ORCID: https://orcid.org/0000-0003-0423-3242)
- Valentina Gnoni (ORCID: https://orcid.org/0000-0003-3181-0842)
- Maria Teresa Dell’Abate (ORCID: https://orcid.org/0000-0003-3364-5721)
- Chiara Zecca (ORCID: https://orcid.org/0000-0002-7855-5714)
- Roberto De Blasi (ORCID: https://orcid.org/0000-0002-8556-8572)
Institutions
- King's College London (GB)
- Ospedale San Paolo (IT)
- Ferioli & Gianotti (Italy) (IT)
- Istituto di Nanotecnologia (IT)
- ASL Roma (IT)
- Innovation Engineering (Italy) (IT)
- University of Bari Aldo Moro (IT)
- University for Foreigners Perugia (IT)
Publication Details
- Journal
- NeuroImage Clinical
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.nicl.2025.103780
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36