Unsupervised learning of metabolic fingerprints from 3D magnetic resonance spectroscopic imaging enables glioma subtype classification.
Saved in:
| Title: | Unsupervised learning of metabolic fingerprints from 3D magnetic resonance spectroscopic imaging enables glioma subtype classification. |
|---|---|
| Authors: | Ungan GS; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Harvard Medical School, Boston., Weiser PJ; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Harvard Medical School, Boston.; Computational Imaging Research Lab-Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria., Dietrich J; Department of Neurology, Massachusetts General Hospital, Boston, Harvard Medical School, Boston., Cahill D; Department of Neurosurgery, Massachusetts General Hospital, Boston, Harvard Medical School, Boston., Andronesi OC; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Harvard Medical School, Boston. |
| Source: | Neuro-oncology advances [Neurooncol Adv] 2025 Oct 23; Vol. 7 (1), pp. vdaf220. Date of Electronic Publication: 2025 Oct 23 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Oxford University Press Country of Publication: England NLM ID: 101755003 Publication Model: eCollection Cited Medium: Internet ISSN: 2632-2498 (Electronic) Linking ISSN: 26322498 NLM ISO Abbreviation: Neurooncol Adv Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
Be the first to leave a comment!
