An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.
Cell Rep Med
; 5(3): 101464, 2024 Mar 19.
Article
en En
| MEDLINE
| ID: mdl-38471504
ABSTRACT
Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on â¼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.
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MEDLINE
Asunto principal:
Neoplasias Encefálicas
/
Aprendizaje Profundo
Idioma:
En
Revista:
Cell Rep Med
Año:
2024
Tipo del documento:
Article