The role of diffusion tractography in refining glial tumor resection

A systematic review of automated methods to perform white matter tract segmentation

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

Development of an educational method to rethink and learn oncological brain surgery in an "a la carte" connectome-based perspective

BACKGROUND

Understanding the structural connectivity of white matter tracts (WMT) and their related functions is a prerequisite to implementing an "a la carte" "connectomic approach" to glioma surgery. However, accessible resources facilitating such an approach are lacking. Here we present an educational method that is readily accessible, simple, and reproducible that enables the visualization of WMTs on individual patient images via an atlas-based approach.

METHODS

Our method uses the patient's own magnetic resonance imaging (MRI) images and consists of three main steps: data conversion, normalization, and visualization; these are accomplished using accessible software packages and WMT atlases. We implement our method on three common cases encountered in glioma surgery: a right supplementary motor area tumor, a left insular tumor, and a left temporal tumor.

RESULTS

Using patient-specific perioperative MRIs with open-sourced and co-registered atlas-derived WMTs, we highlight the critical subnetworks requiring specific surgical monitoring identified intraoperatively using direct electrostimulation mapping with cognitive monitoring. The aim of this didactic method is to provide the neurosurgical oncology community with an accessible and ready-to-use educational tool, enabling neurosurgeons to improve their knowledge of WMTs and to better learn their oncologic cases, especially in glioma surgery using awake mapping.

CONCLUSIONS

Taking no more than 3-5 min per patient and irrespective of their resource settings, we believe that this method will enable junior surgeons to develop an intuition, and a robust 3-dimensional imagery of WMT by regularly applying it to their cases both before and after surgery to develop an "a la carte" connectome-based perspective to glioma surgery.

Characterization and Classification of Spatial White Matter Tract Alteration Patterns in Glioma Patients Using Magnetic Resonance Tractography: A Systematic Review and Meta-Analysis

INTRODUCTION

Magnetic resonance (MR) tractography can be used to study the spatial relations between gliomas and white matter (WM) tracts. Various spatial patterns of WM tract alterations have been described in the literature. We reviewed classification systems of these patterns, and investigated whether low-grade gliomas (LGGs) and high-grade gliomas (HGGs) demonstrate distinct spatial WM tract alteration patterns.

METHODS

We conducted a systematic review and meta-analysis to summarize the evidence regarding MR tractography studies that investigated spatial WM tract alteration patterns in glioma patients.

RESULTS

Eleven studies were included. Overall, four spatial WM tract alteration patterns were reported in the current literature: displacement, infiltration, disruption/destruction and edematous. There was a considerable heterogeneity in the operational definitions of these terms. In a subset of studies, sufficient homogeneity in the classification systems was found to analyze pooled results for the displacement and infiltration patterns. Our meta-analyses suggested that LGGs displaced WM tracts significantly more often than HGGs (n = 259 patients, RR: 1.79, 95% CI [1.14, 2.79], I² = 51%). No significant differences between LGGs and HGGs were found for WM tract infiltration (n = 196 patients, RR: 1.19, 95% CI [0.95, 1.50], I² = 4%).

CONCLUSIONS

The low number of included studies and their considerable methodological heterogeneity emphasize the need for a more uniform classification system to study spatial WM tract alteration patterns using MR tractography. This review provides a first step towards such a classification system, by showing that the current literature is inconclusive and that the ability of fractional anisotropy (FA) to define spatial WM tract alteration patterns should be critically evaluated. We found variations in spatial WM tract alteration patterns between LGGs and HGGs, when specifically examining displacement and infiltration in a subset of the included studies.

Diffusion tensor imaging versus intraoperative subcortical mapping for glioma resection: a systematic review and meta-analysis

Maintaining the integrity of crucial fiber tracts allows functional preservation and improved recovery in patients with glioma resection. Diffusion tensor imaging (DTI) and intraoperative subcortical mapping (ISM) are commonly required for pre- and intraoperative assessment of white matter fibers. This study investigated differences of clinical outcomes in glioma resection aided by DTI or ISM. A comprehensive literature retrieval of the PubMed and Embase databases identified several DTI or ISM studies in 2000-2022. Clinical data, including extent of resection (EOR) and postoperative neurological deficits, was collected and statistically analyzed. Heterogeneity was regressed by a random effect model and the Mann-Whitney U test was used to test statistical significance. Publication bias was assessed by Egger test. A total of 14 studies with a pooled cohort of 1837 patients were included. Patients undergoing DTI-navigated glioma surgery showed a higher rate of gross total resection (GTR) than ISM-assisted surgical resection (67.88%, [95% CI 0.55-0.79] vs. 45.73%, [95% CI 0.29-0.63], P = 0.032). The occurrence of early postoperative functional deficit (35.45%, [95% CI 0.13-0.61] vs. 35.60% [95% CI 0.20-0.53], P = 1.000), late postoperative functional deficit (6.00%, [95% CI 0.02-0.11] vs. 4.91% [95% CI 0.03-0.08], P = 1.000) and severe postoperative functional deficit (2.21%, [95% CI 0-0.08] vs. 5.93% [95% CI 0.01-0.16], P = 0.393) were similar between the DTI and ISM group, respectively. While DTI-navigation resulted in a higher rate of GTR, the occurrence of postoperative neurological deficits between DTI and ISM groups was comparable. Together, these data indicate that both techniques could safely facilitate glioma resection.

Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

PURPOSE

The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal.

METHODS

A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients.

RESULTS

A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B).

CONCLUSIONS

A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity.

DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography

Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose DORIS, a DWI-based deep learning segmentation algorithm. DORIS outputs 10 different tissue classes including WM, GM, CSF, ventricles, and 6 other subcortical structures (putamen, pallidum, hippocampus, caudate, amygdala, and thalamus). DORIS was trained and validated on a wide range of subjects, including 1,000 individuals from 22 to 90 years old from clinical and research DWI acquisitions, from 5 public databases. In the absence of a "true" ground truth in diffusion space, DORIS used a silver standard strategy from Freesurfer output registered onto the DWI. This strategy is extensively evaluated and discussed in the current study. Segmentation maps provided by DORIS are quantitatively compared to Freesurfer and FSL-fast and the impacts on tractography are evaluated. Overall, we show that DORIS is fast, accurate, and reproducible and that DORIS-based tractograms produce bundles with a longer mean length and fewer anatomically implausible streamlines.

Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates

The superior longitudinal fasciculus (SLF) is part of the longitudinal association fiber system, which lays connections between the frontal lobe and other areas of the ipsilateral hemisphere. As a dominant association fiber bundle, it should correspond to a well-defined structure with a clear anatomical definition. However, this is not the case, and a lot of confusion and overlap surrounds this entity. In this review/opinion study, we survey relevant current literature on the topic and try to clarify the definition of SLF in each hemisphere. After a comparison of postmortem dissections and data obtained from diffusion MRI studies, we discuss the specifics of this bundle regarding its anatomical landmarks, differences in lateralization, as well as individual variability. We also discuss the confusion regarding the arcuate fasciculus in relation to the SLF. Finally, we recommend a nomenclature based on the findings exposed in this review and finalize with a discussion on relevant functional correlates of the structure.

Inferior fronto-occipital fascicle displacement in temporoinsular gliomas using diffusion tensor imaging

Background and Purpose

Brain tumors can result in displacement or destruction of important white matter tracts such as the inferior fronto‐occipital fascicle (IFOF). Diffusion tensor imaging (DTI) can assess the extent of this effect and potentially provide neurosurgeons with an accurate map to guide tumor resection; analyze IFOF displacement patterns in temporoinsular gliomas based on tumor grading and topography in the temporal lobe; and assess whether these patterns follow a predictable pattern, to assist in maximal tumor resection while preserving IFOF function.

Methods

Thirty‐four patients with temporal gliomas and available presurgical MRI were recruited. Twenty‐two had insula infiltration. DTI deterministic region of interest (ROI)‐based tractography was performed using commercial software. Tumor topographic imaging characteristics analyzed were as follows: location in the temporal lobe and extent of extratemporal involvement. Qualitative tractographic data obtained from directional DTI color maps included type of involvement (displaced/edematous‐infiltrated/destroyed) and displacement direction. Quantitative tractographic data of ipsi‐ and contralateral IFOF included whole tract volume, fractional anisotropy, and fractional anisotropy of a 2‐dimensional coronal ROI on the tract at the point of maximum tumor involvement.

Results

The most common tract involvement pattern was edematous/infiltrative displacement. Displacement patterns depended on main tumor location in the temporal lobe and presence of insular involvement. All tumors showed superior displacement pattern. In lateral tumors, displacement tendency was medial. In medial tumors, displacement tendency was lateral. When we add insular involvement, the tendency was more medial displacement. A qualitative and quantitative assessment supported these results.

Conclusions

IFOF displacement patterns are reproducible and suitable for temporoinsular gliomas presurgical planning.

The third branch of the superior longitudinal system

One of the major associative fiber pathways in the brain is the superior longitudinal system. This review discusses the current knowledge gained from studies on the third branch of the superior longitudinal system (SLS) regarding its anatomy, functional role in healthy individuals, results from lesion-symptom mapping studies and intraoperative electrostimulation studies. The results of these studies clearly indicate that the third branch of the SLS is a distinct pathway, as seen both from a functional and anatomical perspective. The third branch of the SLS should be distinguished from the long segment of the arcuate fasciculus, that courses along its trajectory but seems implicated in different functions. Moreover, these studies also provide substantial evidence that the right and left third branch of the SLS have different functional roles. Finally, a hypothesis for an integrated anatomo-functional model is proposed, that describes three subcomponents of the third branch of the superior longitudinal system.

A systematic review of the use of subcortical intraoperative electrical stimulation mapping for monitoring of executive deficits and neglect: what is the evidence so far?

BACKGROUND

Over the past decade, the functional importance of white matter pathways has been increasingly acknowledged in neurosurgical planning. A method to directly study anatomo-functional correlations is direct electrical stimulation (DES). DES has been widely accepted by neurosurgeons as a reliable tool to minimize the occurrence of permanent postoperative motor, vision, and language deficits. In recent years, DES has also been used for stimulation mapping of other cognitive functions, such as executive functions and visuospatial awareness.

METHODS

The aim of this review is to summarize the evidence so far from DES studies on subcortical pathways that are involved in visuospatial awareness and in the following three executive functions: (1) inhibitory control, (2) working memory, and (3) cognitive flexibility.

RESULTS

Eleven articles reported on intraoperative electrical stimulation of white matter pathways to map the cognitive functions and explicitly clarified which subcortical tract was stimulated. The results indicate that the right SLF-II is involved in visuospatial awareness, the left SLF-III and possibly the right SLF-I are involved in working memory, and the cingulum is involved in cognitive flexibility.

CONCLUSIONS

We were unable to draw any more specific conclusions, nor unequivocally establish the critical involvement of pathways in executive functions or visuospatial awareness due to the heterogeneity of the study types and methods, and the limited number of studies that assessed these relationships. Possible approaches for future research to obtain converging and more definite evidence for the involvement of pathways in specific cognitive functions are discussed.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

White matter variability, cognition, and disorders: a systematic review

Inter-individual differences can inform treatment procedures and—if accounted for—have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract–function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

Diffusion tractography for awake craniotomy: accuracy and factors affecting specificity

Introduction

Despite evidence of correspondence with intraoperative stimulation, there remains limited data on MRI diffusion tractography (DT)’s sensitivity to predict morbidity after neurosurgical oncology treatment. Our aims were: (1) evaluate DT against subcortical stimulation mapping and performance changes during and after awake neurosurgery; (2) evaluate utility of early post-operative DT to predict recovery from post-surgical deficits.

Methods

We retrospectively reviewed our first 100 awake neurosurgery procedures using DT- neuronavigation. Intra-operative stimulation and performance outcomes were assessed to classify DT predictions for sensitivity and specificity calculations. Post-operative DT data, available in 51 patients, were inspected for tract damage.

Results

91 adult brain tumor patients (mean 49.2 years, 43 women) underwent 100 awake surgeries with subcortical stimulation between 2014 and 2019. Sensitivity and specificity of pre-operative DT predictions were 92.2% and 69.2%, varying among tracts. Post-operative deficits occurred after 41 procedures (39%), but were prolonged (> 3 months) in only 4 patients (4%). Post-operative DT in general confirmed surgical preservation of tracts. Post-operative DT anticipated complete recovery in a patient with supplementary motor area syndrome, and indicated infarct-related damage to corticospinal fibers associated with delayed, partial recovery in a second patient.

Conclusions

Pre-operative DT provided very accurate predictions of the spatial location of tracts in relation to a tumor. As expected, however, the presence of a tract did not inform its functional status, resulting in variable DT specificity among individual tracts. While prolonged deficits were rare, DT in the immediate post-operative period offered additional potential to monitor neurological deficits and anticipate recovery potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11060-021-03795-7.

Regulation of temozolomide resistance in glioma cells via the RIP2/NF-κB/MGMT pathway

BACKGROUND

Temozolomide (TMZ) is a first-line chemotherapy drug for the treatment of malignant glioma and resistance to it poses a major challenge. Receptor-interacting protein 2 (RIP2) is associated with the malignant character of cancer cells. However, it remains unclear whether RIP2 is involved in TMZ resistance in glioma.

METHODS

RIP2 expression was inhibited in TMZ-resistant glioma cells and normal glioma cells by using small interfering RNA (siRNA) against RIP2. Plasmid transfection method was used to overexpress RIP2. Cell counting kit-8 assays were performed to evaluate cell viability. Western blotting or immunofluorescence was performed to determine RIP2, NF-κB, and MGMT expression in cells. Flow cytometry was used to investigate cell apoptosis. TMZ-resistant glioma xenograft models were established to evaluate the role of the RIP2/NF-κB/MGMT signaling pathway in drug resistance.

RESULTS

We observed that RIP2 expression was upregulated in TMZ-resistant glioma cells, whereas silencing of RIP2 expression enhanced cellular sensitivity to TMZ. Similarly, upon the induction of RIP2 overexpression, glioma cells developed resistance to TMZ. The molecular mechanism underlying the process indicated that RIP2 can activate the NF-κB signaling pathway and upregulate the expression of O6-methylguanine-DNA methyltransferase (MGMT), following which the glioma cells develop drug resistance. In the TMZ-resistant glioma xenograft model, treatment with JSH-23 (an NF-κB inhibitor) and lomeguatrib (an MGMT inhibitor) could enhance the sensitivity of the transplanted tumor to TMZ.

CONCLUSION

We report that the RIP2/NF-κB/MGMT signaling pathway is involved in the regulation of TMZ resistance. Interference with NF-κB or MGMT activity could constitute a novel strategy for the treatment of RIP2-positive TMZ-resistant glioma.

Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks

White matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI) provides an important tool for the analysis of brain development, function, and disease. Deep learning based methods of WM tract segmentation have been proposed, which greatly improve the accuracy of the segmentation. However, the training of the deep networks usually requires a large number of manual delineations of WM tracts, which can be especially difficult to obtain and unavailable in many scenarios. Therefore, in this work, we explore how to perform deep learning based WM tract segmentation when annotated training data is scarce. To this end, we seek to exploit the abundant unannotated dMRI data in the self-supervised learning framework. From the unannotated data, knowledge about image context can be learned with pretext tasks that do not require manual annotations. Specifically, a deep network can be pretrained for the pretext task, and the knowledge learned from the pretext task is then transferred to the subsequent WM tract segmentation task with only a small number of annotated scans via fine-tuning. We explore two designs of pretext tasks that are related to WM tracts. The first pretext task predicts the density map of fiber streamlines, which are representations of generic WM pathways, and the training data can be obtained automatically with tractography. The second pretext task learns to mimic the results of registration-based WM tract segmentation, which, although inaccurate, is more relevant to WM tract segmentation and provides a good target for learning context knowledge. Then, we combine the two pretext tasks and develop a nested self-supervised learning strategy. In the nested self-supervised learning strategy, the first pretext task provides initial knowledge for the second pretext task, and the knowledge learned from the second pretext task with the initial knowledge is transferred to the target WM tract segmentation task via fine-tuning. To evaluate the proposed method, experiments were performed on brain dMRI scans from the Human Connectome Project dataset with various experimental settings. The results show that the proposed method improves the performance of WM tract segmentation when tract annotations are scarce.

Enhanced Fiber Tractography Using Edema Correction: Application and Evaluation in High-Grade Gliomas

BACKGROUND

A limitation of diffusion tensor imaging (DTI)-based tractography is peritumoral edema that confounds traditional diffusion-based magnetic resonance metrics.

OBJECTIVE

To augment fiber-tracking through peritumoral regions by performing novel edema correction on clinically feasible DTI acquisitions and assess the accuracy of the fiber-tracks using intraoperative stimulation mapping (ISM), task-based functional magnetic resonance imaging (fMRI) activation maps, and postoperative follow-up as reference standards.

METHODS

Edema correction, using our bi-compartment free water modeling algorithm (FERNET), was performed on clinically acquired DTI data from a cohort of 10 patients presenting with suspected high-grade glioma and peritumoral edema in proximity to and/or infiltrating language or motor pathways. Deterministic fiber-tracking was then performed on the corrected and uncorrected DTI to identify tracts pertaining to the eloquent region involved (language or motor). Tracking results were compared visually and quantitatively using mean fiber count, voxel count, and mean fiber length. The tracts through the edematous region were verified based on overlay with the corresponding motor or language task-based fMRI activation maps and intraoperative ISM points, as well as at time points after surgery when peritumoral edema had subsided.

RESULTS

Volume and number of fibers increased with application of edema correction; concordantly, mean fractional anisotropy decreased. Overlay with functional activation maps and ISM-verified eloquence of the increased fibers. Comparison with postsurgical follow-up scans with lower edema further confirmed the accuracy of the tracts.

CONCLUSION

This method of edema correction can be applied to standard clinical DTI to improve visualization of motor and language tracts in patients with glioma-associated peritumoral edema.

From Neurosurgical Planning to Histopathological Brain Tumor Characterization: Potentialities of Arcuate Fasciculus Along-Tract Diffusion Tensor Imaging Tractography Measures

Background: Tractography has been widely adopted to improve brain gliomas' surgical planning and guide their resection. This study aimed to evaluate state-of-the-art of arcuate fasciculus (AF) tractography for surgical planning and explore the role of along-tract analyses in vivo for characterizing tumor histopathology.

Methods: High angular resolution diffusion imaging (HARDI) images were acquired for nine patients with tumors located in or near language areas (age: 41 ± 14 years, mean ± standard deviation; five males) and 32 healthy volunteers (age: 39 ± 16 years; 16 males). Phonemic fluency task fMRI was acquired preoperatively for patients. AF tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. Along-tract analyses were performed, dividing the AF into 15 segments along the length of the tract defined using the Laplacian operator. For each AF segment, diffusion tensor imaging (DTI) measures were compared with those obtained in healthy controls (HCs). The hemispheric laterality index (LI) was calculated from language task fMRI activations in the frontal, parietal, and temporal lobe parcellations. Tumors were grouped into low/high grade (LG/HG).

Results: Four tumors were LG gliomas (one dysembryoplastic neuroepithelial tumor and three glioma grade II) and five HG gliomas (two grade III and three grade IV). For LG tumors, gross total removal was achieved in all but one case, for HG in two patients. Tractography identified the AF trajectory in all cases. Four along-tract DTI measures potentially discriminated LG and HG tumor patients (false discovery rate < 0.1): the number of abnormal MD and RD segments, median AD, and MD measures. Both a higher number of abnormal AF segments and a higher AD and MD measures were associated with HG tumor patients. Moreover, correlations (unadjusted p < 0.05) were found between the parietal lobe LI and the DTI measures, which discriminated between LG and HG tumor patients. In particular, a more rightward parietal lobe activation (LI < 0) correlated with a higher number of abnormal MD segments (R = −0.732) and RD segments (R = −0.724).

Conclusions: AF tractography allows to detect the course of the tract, favoring the safer-as-possible tumor resection. Our preliminary study shows that along-tract DTI metrics can provide useful information for differentiating LG and HG tumors during pre-surgical tumor characterization.