Prof. Dr. Alexander Radbruch
Clinic for Neuroradiology
Alexander.Radbruch@ukbonn.de View member: Prof. Dr. Alexander Radbruch
Neuro-oncology
BACKGROUND: Differentiating progressive disease (PD) from treatment-related effects (TRE) in glioblastoma remains challenging, particularly at single time point evaluations. TRE can occur at any disease stage, and its underlying biology is poorly understood. This study evaluates the clinical feasibility and diagnostic performance of amide proton transfer-weighted (APTw) MRI in this challenge.
METHODS: Following the integration of APTw MRI into the routine clinical workflow for brain tumor imaging, we screened a total of 870 scans from 626 patients. APTw signal (voxel-based measurement) was automatically quantified in gadolinium-enhanced T1w and FLAIR regions of interest using a deep learning-based approach for 3D tumor segmentations. PD and TRE were compared using unpaired t-tests, and diagnostic accuracy was assessed via ROC- and logistic regression analysis.
RESULTS: Among 256 MRI scans of 143 patients with glioblastoma, 65 scans showed PD (n = 42) or TRE (n = 23). The median APTw signal was higher in PD (2.23%) vs TRE (1.76%; p = 0.001). ROC analysis showed an area under the curve (AUC) of 0.82. In patients with early PD or TRE (<6 months post-radiotherapy), the AUC increased to 0.93. Anti-angiogenic therapy decreased APTw signal (p < 0.01). Combining APTw MRI with DWI and PWI improved diagnostic accuracy (AUC = 0.90).
CONCLUSIONS: APTw MRI is a non-invasive imaging tool that is feasible for clinical routine and aids in differentiation of early progression from pseudoprogression in glioblastoma. Its diagnostic accuracy decreases with application of anti-angiogenic treatment and at later follow-up time points. Highest diagnostic accuracy was found in a multimodal approach combining APTw MRI, PWI and DWI.
© The Author(s) 2025. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.
PMID: 41233976
Clinic for Neuroradiology
Alexander.Radbruch@ukbonn.de View member: Prof. Dr. Alexander Radbruch