Non-invasive identification of mesenchymal glioblastoma using quantitative radiomic features from advanced diffusion MRI: a preclinical-to-clinical transfer learning strategy
We defined a machine learning algorithm that, starting from subgroup-specific glioblastoma xenografts, reliably identifies the mesenchymal affiliation of glioblastoma patients. The specific dMRI features selected from experimental preclinical models of glioblastoma hold a remarkable predictive value. The same features provide insights into subgroup-restricted tumor tissue microstructure and its relationship with the malignant behavior of mesenchymal glioblastomas.
Authors
- Gianluca Brugnara (ORCID: https://orcid.org/0000-0003-2461-1407)
- Valentina Pieri (ORCID: https://orcid.org/0000-0001-9244-3834)
- Nicolò Pecco (ORCID: https://orcid.org/0000-0002-6367-7903)
- Rossella Galli (ORCID: https://orcid.org/0000-0001-9023-7033)
- Manuela Cominelli (ORCID: https://orcid.org/0000-0002-4520-6104)
- Luisa Altabella (ORCID: https://orcid.org/0000-0002-4816-6945)
- Alberto L. Gallotti (ORCID: https://orcid.org/0000-0001-9698-7919)
- Pietro Luigi Poliani (ORCID: https://orcid.org/0000-0002-5662-8978)
- Antonella Castellano (ORCID: https://orcid.org/0000-0002-4137-9016)
- Filippo Gagliardi (ORCID: https://orcid.org/0000-0002-2296-1764)
- Andrei Fodor (ORCID: https://orcid.org/0000-0002-9285-4390)
- Andrea Falini (ORCID: https://orcid.org/0000-0002-1461-8755)
- Pietro Mortini (ORCID: https://orcid.org/0000-0003-2320-2281)
- Ilaria Pagano (ORCID: https://orcid.org/0000-0001-6122-0080)
- Marcella Callea (ORCID: https://orcid.org/0000-0003-0890-6651)
Institutions
- Vita-Salute San Raffaele University (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT)
- University of Brescia (IT)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1186/s41747-025-00652-4
- Citations
- 1
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.63
Funders
- Ministero della Salute