Background
Advances in white matter tractography enhance neurosurgical planning and glioma resection, but is limited by biological variables such as edema, mass effect, and tract infiltration, or selection biases related to regions of interest (ROIs) or fractional anisotropy (FA) values.
Objective
To provide an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration, as well as providing a consistent, accurate method of fiber tractography.
Methods
An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from six healthy participants. Fibers of a test set (including three healthy participants and ten patients with brain tumors) were clustered adaptively using this atlas. Reliability of identified tracts in both groups was assessed by comparison with two experts, using Cohen's kappa to quantify concurrence. We evaluated six major fiber bundles: cingulum bundle (CB), fornix (FR), uncinate fasciculus (UF), arcuate fasciculus (AF), inferior fronto-occipital fasciculus (IFOF), and inferior longitudinal fasciculus (ILF) – the latter three tracts mediating language function.
Results
The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful to provide an initialization to guide the expert with identification of the specific tract of interest.
Conclusion
We report a reliable paradigm for automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections.