Predicting the displacement of cranial nerves by tumors could make surgery safer and the outcome better. Recent advances in imaging and processing have overcome some of the limits associated with cranial nerve tractography, such as spatial resolution and fiber crossing. Among others, probabilistic algorithms yield to a more accurate depiction of cranial nerve trajectories. OBJECTIVE: To report how cranial nerve probabilistic tractography can help the surgical strategy in a series of various skull base tumors. METHODS: After distortion correction and region of interest seeding, a probabilistic tractography algorithm used the constrained spherical deconvolution model and attempted the reconstruction of cranial nerve trajectories in both healthy and displaced conditions. RESULTS: Sixty-two patients were included and presented: vestibular schwannomas (n = 33); cerebellopontine angle meningiomas (n = 15); arachnoid or epidermoid cysts (n = 6); cavernous sinus and lower nerves schwannomas (n = 4); and other tumors (n = 4). For each patient, at least one 'displaced' cranial nerve was not clearly identified on classical anatomical MRI images. All 372 cranial nerves were successfully tracked on each healthy side; among the 175 cranial nerves considered 'displaced' by tumors, 152 (87%) were successfully tracked. Among the 127 displaced nerves of operated patients (n = 51), their position was confirmed intraoperatively for 118 (93%) of them. Conditions that led to tractography failure were detailed. On the basis of tractography, the surgical strategy was adjusted for 44 patients (71%). CONCLUSION: This study reports a cranial nerve probabilistic tractography pipeline that can: predict the position of most cranial nerves displaced by skull base tumors, help the surgical strategy, and thus be a pertinent tool for future routine clinical application.
- Cranial nerves
- Diffusion imaging