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Updated: Apr 14, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Extending voxel-based lesion mapping to neurosurgical oncology: applications in intracranial meningiomas
Aldo Spolaore1, Sophie Wang1, Kathrin Machetanz1
1Department of Neurosurgery and Neurotechnology, Eberhard Karls University, Tuebingen, Germany.
Background:
Voxel-based lesion symptom mapping (VLSM) is a powerful neuroimaging technique for linking lesion location to clinical variables, widely used in stroke research. However, its methodological application in neuro-oncology, particularly for extra-axial tumors such as meningiomas, remains underexplored. This study aims to establish a methodological framework for extending VLSM to neurosurgical oncology by adapting it to the specific anatomical and biological features of meningiomas.
Methods:
In this retrospective single-center study, we analyzed preoperative MRI data from 350 patients who underwent surgical resection of intracranial meningiomas. Tumors were semi-automatically segmented, normalized to MNI space, and mapped to identify the dural attachment zone (DAZ). VLSM was performed using univariable linear regression and corrected for multiple comparisons to assess spatial correlations with tumor histology, preoperative symptoms, surgical outcomes, and postoperative deficits.
Results:
Meningiomas clustered predominantly along the anterior and middle skull base, falcine region, and sphenoid wing. WHO grade 2 meningiomas showed significant spatial clustering at the cerebral convexity and frontal base. Tumors involving the perirolandic area were associated with higher rates of pre- and postoperative motor deficits. Incomplete resections were more frequent in meningiomas of the medial sphenoid wing. VLSM results based on DAZ improved anatomical specificity and reduced volume-related bias compared to analyses using total tumor volume.
Conclusion:
VLSM offers a robust and anatomically precise framework for studying spatial patterns in meningioma biology and outcomes. Focusing on the DAZ enhances interpretability by reducing volume bias. This approach enables refined risk stratification and supports multicenter collaboration for developing spatially informed, individualized treatment strategies in neurosurgical oncology.

