Tracking the Corticospinal Tract in Patients With High-Grade Glioma: Clinical Evaluation of Multi-Level Fiber Tracking and Comparison to Conventional Deterministic Approaches

Abstract

While the diagnosis of high-grade glioma (HGG) is still associated with a considerably poor prognosis, neurosurgical tumor resection provides an opportunity for prolonged survival and improved quality of life for affected patients. However, successful tumor resection is dependent on a proper surgical planning to avoid surgery-induced functional deficits whilst achieving a maximum extent of resection (EOR). With diffusion magnetic resonance imaging (MRI) providing insight into individual white matter neuroanatomy, the challenge remains to disentangle that information as correctly and as completely as possible. In particular, due to the lack of sensitivity and accuracy, the clinical value of widely used diffusion tensor imaging (DTI)-based tractography is increasingly questioned. We evaluated whether the recently developed multi-level fiber tracking (MLFT) technique can improve tractography of the corticospinal tract (CST) in patients with motor-eloquent HGGs. Forty patients with therapy-naïve HGGs (mean age: 62.6 ± 13.4 years, 57.5% males) and preoperative diffusion MRI [repetition time (TR)/echo time (TE): 5000/78 ms, voxel size: 2x2x2 mm3, one volume at b=0 s/mm2, 32 volumes at b=1000 s/mm2] underwent reconstruction of the CST of the tumor-affected and unaffected hemispheres using MLFT in addition to deterministic DTI-based and deterministic constrained spherical deconvolution (CSD)-based fiber tractography. The brain stem was used as a seeding region, with a motor cortex mask serving as a target region for MLFT and a region of interest (ROI) for the other two algorithms. Application of the MLFT method substantially improved bundle reconstruction, leading to CST bundles with higher radial extent compared to the two other algorithms (delineation of CST fanning with a wider range; median radial extent for tumor-affected vs. unaffected hemisphere – DTI: 19.46° vs. 18.99°, p=0.8931; CSD: 30.54° vs. 27.63°, p=0.0546; MLFT: 81.17° vs. 74.59°, p=0.0134). In addition, reconstructions by MLFT and CSD-based tractography nearly completely included respective bundles derived from DTI-based tractography, which was however favorable for MLFT compared to CSD-based tractography (median coverage of the DTI-based CST for affected vs. unaffected hemispheres – CSD: 68.16% vs. 77.59%, p=0.0075; MLFT: 93.09% vs. 95.49%; p=0.0046). Thus, a more complete picture of the CST in patients with motor-eloquent HGGs might be achieved based on routinely acquired diffusion MRI data using MLFT.

Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping

“…MLFT has been recently introduced and is a bundle-specific algorithm that reconstructs fiber bundles as multilevel structures. 27,28 The algorithm requires seed and target regions (ie, the ipsilateral anterior pontine brainstem level and the nTMS-derived cortical motor map) and a maximum number of levels. 27,28 At each iteration, streamline propagation is performed using a deterministic CSD-based algorithm.…”

“…27,28 The algorithm requires seed and target regions (ie, the ipsilateral anterior pontine brainstem level and the nTMS-derived cortical motor map) and a maximum number of levels. 27,28 At each iteration, streamline propagation is performed using a deterministic CSD-based algorithm. At the end of each iteration, FOD peaks of the points along pathways that did not reach the target region are used as initial directions at the following iteration.…”

“…25,26 Multilevel fiber tractography (MLFT) has been developed from CSD-based tractography and similarly propagates fiber pathways on the basis of FOD peaks, with the main advancement that MLFT proposes that FOD peaks do not solely reflect crossing fibers but may also reflect high angular deviation of fibers or their branching. 27,28 A previous study has demonstrated that MLFT improved reconstruction of the corticospinal tract (CST) in patients with motor-eloquent high-grade gliomas by generating fiber bundles with higher radial extent (ie, delineation of CST fanning with a wider range) compared with DTI as well as CSDbased deterministic CST tractography, thus potentially showing a more complete picture of the actual CST architecture. 27 Yet, without optimal seeding of the ROI for tractography, MLFT would be at considerable risk of reconstructing false-positive WM pathways, given that it can also include acute angles of fiber courses, which might be particularly frequently observed among patients with brain tumors due to the space-occupying effect that may lead to fiber diversion and compression.…”

“…27,28 A previous study has demonstrated that MLFT improved reconstruction of the corticospinal tract (CST) in patients with motor-eloquent high-grade gliomas by generating fiber bundles with higher radial extent (ie, delineation of CST fanning with a wider range) compared with DTI as well as CSDbased deterministic CST tractography, thus potentially showing a more complete picture of the actual CST architecture. 27 Yet, without optimal seeding of the ROI for tractography, MLFT would be at considerable risk of reconstructing false-positive WM pathways, given that it can also include acute angles of fiber courses, which might be particularly frequently observed among patients with brain tumors due to the space-occupying effect that may lead to fiber diversion and compression. 27 The issue of optimal ROI seeding for CST reconstruction may be addressed by combining MLFT with preoperative functional mapping, such as navigated transcranial magnetic stimulation (nTMS).…”

“…27 Yet, without optimal seeding of the ROI for tractography, MLFT would be at considerable risk of reconstructing false-positive WM pathways, given that it can also include acute angles of fiber courses, which might be particularly frequently observed among patients with brain tumors due to the space-occupying effect that may lead to fiber diversion and compression. 27 The issue of optimal ROI seeding for CST reconstruction may be addressed by combining MLFT with preoperative functional mapping, such as navigated transcranial magnetic stimulation (nTMS). In essence, nTMS can target neurostimulation to the brain with subcentimeter precision and enables the spatial identification and demarcation of the motor cortex in relation to a lesion.…”