Background: Radiologically presumed diffuse lower-grade glioma (dLGG) are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. The aim of this study was to test the clinical usefulness of deep learning (DL) in mutation prediction in patients with radiologically presumed dLGG.
Methods: Three hundred and fourteen patients were retrospectively recruited from 6 neurosurgical departments in Sweden, Norway, France, Austria, and the United States.
Neuropathol Appl Neurobiol
December 2024
Aims: FGFR-fused central nervous system (CNS) tumours are rare and are usually within the glioneuronal and neuronal tumours or the paediatric-type diffuse low-grade glioma spectrum. Among this spectrum, FGFR2 fusion has been documented in tumours classified by DNA-methylation profiling as polymorphous low-grade neuroepithelial tumours of the young (PLNTY), a recently described tumour type. However, FGFR2 fusions have also been reported in glioneuronal tumours, highlighting the overlapping diagnostic criteria and challenges.
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