Tractography enables us to reconstruct white matter (wm) fibre bundles from MRI, providing crucial insights into anatomical structures. It has become essential for planning and risk stratification in neurosurgery. Peritumoral tractography is challenging due to arising edema, demyelination and infiltration, yet vital in patients with gliomas in the left perisylvian area, possibly causing aphasia. Several deterministic and probabilistic algorithms have been developed, that model wm based on tensors (DTI) or spherical harmonic functions (CSD). Here we compared tractograms derived by common algorithms, examining how they differ in patients with perisylvian gliomas.
We included n=62 presurgical patients with left perisylvian gliomas (24 females, 38 males, average age=53,5±15.4, age range 21–83). All were right-handed and had an initial diagnosis of supratentorial, unilateral WHO grade II (6), III (20), or IV (36) glioma. We derived the language relevant tractograms, AF, IFOF, ILF and UF, on both hemispheres employing DTI- (tensor_det, tensor_prob) and CSD-based (SD_stream, iFOD2) algorithms via TractSeg. We compared the results by streamline length, volume and dice scores.
DTI- and CSD-algorithms failed in 16% and 13% peritumorally, respectively, and in 2% and 0% on the healthy hemisphere. Streamlines in the pathologic side were significantly 3% shorter and tract volumes 10% smaller than on the healthy one for all algorithms. Tract volumes vary a lot for different algorithms, but their ratios are similar in both healthy and pathologic hemisphere. Using the largest volumes generated by iFOD2 as reference, we find the ratios 0.47, 0.33 and 0.28 for SD_stream, tensor_prob and tensor_det, respectively. The dice scores for the pathological side revealed that DTI tractograms are more similar (0.85±0.10) than CSD-based ones (0.51±0.10). iFOD2 tracts differ substantially from tensor_det tracts (0.38±0.10).
Tractograms generated by different algorithms vary in size and shape. These variations are similar in healthy and pathological hemispheres. CSD-based algorithms are less likely to fail and, thus, are useful in neurosurgical practice. Since DTI-based algorithms tend to underestimate and iFOD2 to overestimate tracts, integrating both in clinical practice may offer more precise results, finding a middle ground.