Radiomics in Medulloblastoma: Advancing Non-Invasive Diagnosis, Molecular Subtyping, and Prognostication
Motivation: As medulloblastoma (MB) management advances, radiomics provides a non-invasive avenue to improve diagnosis, subtyping, and prognostication. By consolidating current evidence, this study clarifies radiomics' capability to address these critical aspects effectively. Goal(s): To systematically evaluate radiomics' accuracy in MB diagnosis, subtype differentiation, and prognostic insights, identifying specific imaging markers that could strengthen clinical decision-making. Approach: We analyzed 21 qualified studies to assess the efficacy of radiomic features. Results: Radiomics showed encouraging classification accuracies, with some models reaching up to 95% in differentiating MB fromothertumors. CE-T1, GLCM, and ADC metrics provided valuable insights into subtype characteristics and potential prognostic indicators. Impact: This study underscores radiomics' potential to refine non-invasive MB management, aiding clinicians in earlier, more precise diagnoses and individualized treatment plans. It highlights encouraging imaging markers that may inspire future research on radiomics-driven clinical protocols and patient outcomes.
Authors
- Fardin Samadi Khoshe Mehr (ORCID: https://orcid.org/0000-0002-2502-0329)
- MirHojjat Khorasanizadeh
- Ataollah Shahbandi (ORCID: https://orcid.org/0000-0003-1042-8580)
- Samuel Berchi Kankam (ORCID: https://orcid.org/0000-0002-1682-160X)
- Lola B. Chambless (ORCID: https://orcid.org/0000-0001-5864-5387)
- Maryam Fotouhi (ORCID: https://orcid.org/0000-0003-4569-7094)
- M. Shahla
- Seyed Mobin Nouredini
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/2090
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00