MRI-Based Radiomics for Non-Invasive Prediction of Molecular Biomarkers in Gliomas
Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor.
Authors
- Amedeo Piazza (ORCID: https://orcid.org/0000-0001-5360-5810)
- Gianluca Grimod
- Karen Mapelli (ORCID: https://orcid.org/0009-0001-5957-8689)
- Marco Maria Fontanella
- Pier Paolo Panciani
- Edoardo Agosti
Institutions
- University of Pavia (IT)
- Policlinico San Matteo Fondazione (IT)
- University of Brescia (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Cancers
- Published
- 2026-02-02
- DOI
- https://doi.org/10.3390/cancers18030491
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 31.92