Automated diffusion analysis for non-invasive prediction of IDH genotype in WHO grade 2-3 gliomas
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Authors
Wu, Jiaming
Thust, Stefanie C.
Wastling, Stephen J.
Abdalla, Gehad
Benenati, Massimo
Maynard, John A.
Brandner, Sebastian
Carrasco, Ferran Prados
Barkhof, Frederik
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Issue Date
2025
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Article
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Abstract
BACKGROUND AND PURPOSE: Glioma molecular characterization is essential for risk stratification and treatment planning. Noninvasive imaging biomarkers such as ADC values have shown potential for predicting glioma genotypes. However, manual segmentation of gliomas is time-consuming and operator-dependent. To address this limitation, we aimed to establish a single-sequence-derived automatic ADC extraction pipeline by using T2-weighted imaging to support glioma MATERIALS AND METHODS: Glioma volumes from a hospital data set (University College London Hospitals [UCLH]; RESULTS: nnUNet segmentation achieved a median Dice of 0.85 on BraTS data, and 0.83 on UCLH data. For the best performing metric (normalized CONCLUSIONS: The T2-weighted trained nnUNet algorithm achieved ADC readouts for IDH genotyping with a performance statistically equivalent to human observers. This approach could support rapid ADC-based identification of glioblastoma at an early disease stage, even with limited input data. Artificial intelligence level of evidence: 5A.
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American Journal of Neuroradiology
Volume
46
Issue
10