Involvement of White Matter Language Tracts in Glioma: Clinical Implications, Operative Management, and Functional Recovery After Injury

Attenuation coefficient as a tool to detect changes in the white matter of the rat brain caused by different types of gliomas and irradiation

In the present work, we carried out a comparative study of the attenuation coefficient of the white matter of the rat brain during the growth of glial tumors characterized by different degrees of malignancy (glioblastoma 101/8, astrocytoma 10-17-2, glioma C6) and during irradiation. We demonstrated that some tumor models cause a pronounced decrease in white matter attenuation coefficient values due to infiltration of tumor cells, myelinated fiber destruction, and edema. In contrast, other tumors cause compression of the myelinated fibers of the corpus callosum without their ruptures and prominent invasion of tumor cells, which preserved the attenuation coefficient values changeless. In addition, for the first time, the possibility of using the attenuation coefficient to detect late radiation-induced changes in white matter characterized by focal development of edema, disruption of the integrity of myelinated fibers, and a decrease in the amount of oligodendrocytes and differentiation of these areas from tumor tissue and healthy white matter has been demonstrated. The results indicate the promise of using the attenuation coefficient estimated from OCT data for in vivo assessment of the degree of destruction of peritumoral white matter or its compression, which makes this method useful not only in primary resections but also in repeated surgical interventions for recurrent tumors.

Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine

PURPOSE OF REVIEW

Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.

RECENT FINDINGS

Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.

The Association Between Task Complexity and Cortical Language Mapping Accuracy

BACKGROUND AND OBJECTIVES

Direct cortical stimulation (DCS) mapping enables the identification of functional language regions within and around gliomas before tumor resection. Intraoperative mapping is required because glioma-infiltrated cortex engages in synchronous activity during task performance in a manner similar to normal-appearing cortex but has decreased ability to encode information for complex tasks. It is unknown whether task complexity influenced DCS mapping results. We aim to understand correlations between audiovisual picture naming (PN) task complexity and DCS error rate. We also asked what functional and oncological factors might be associated with higher rates of erroneous responses.

METHODS

We retrospectively reviewed intraoperative PN and word reading (WR) task performance during awake DCS language mapping for resection of dominant hemisphere World Health Organization grade 2 to 4 gliomas. The complexity of word tested in PN/WR tasks, patient characteristics, and tumor characteristics were compared between correct and incorrect trials.

RESULTS

Between 2017 and 2021, 74 patients met inclusion criteria. At median 18.6 months of follow-up, 73.0% were alive and 52.7% remained recurrence-free. A total of 2643 PN and 978 WR trials were analyzed. A greater number of syllables in PN was associated with a higher DCS error rate (P = .001). Multivariate logistic regression found that each additional syllable in PN tasks independently increased odds of error by 2.40 (P < .001). Older age was also an independent correlate of higher error rate (P < .043). World Health Organization grade did not correlate with error rate (P = .866). More severe language impairment before surgery correlated with worse performance on more complex intraoperative tasks (P < .001). A higher error rate on PN testing did not correlate with lower extent of glioma resection (P = .949).

CONCLUSION

Word complexity, quantified by the number of syllables, is associated with higher error rates for intraoperative PN tasks but does not affect extent of resection.

Delineation of three-dimensional tumor margins based on normalized absolute difference mapping via volumetric optical coherence tomography

The extent of surgical resection is an important prognostic factor in the treatment of patients with glioblastoma. Optical coherence tomography (OCT) imaging is one of the adjunctive methods available to achieve the maximal surgical resection. In this study, the tumor margins were visualized with the OCT image obtained from a murine glioma model. A commercialized human glioblastoma cell line (U-87) was employed to develop the orthotopic murine glioma model. A swept-source OCT (SS-OCT) system of 1300 nm was used for three-dimensional imaging. Based on the OCT intensity signal, which was obtained via accumulation of each A-scan data, an en-face optical attenuation coefficient (OAC) map was drawn. Due to the limited working distance of the focused beam, OAC values decrease with depth, and using the OAC difference in the superficial area was chosen to outline the tumor boundary, presenting a challenge in analyzing the tumor margin along the depth direction. To overcome this and enable three-dimensional tumor margin detection, we converted the en-face OAC map into an en-face difference map with x- and y-directions and computed the normalized absolute difference (NAD) at each depth to construct a volumetric NAD map, which was compared with the corresponding H&E-stained image. The proposed method successfully revealed the tumor margin along the peripheral boundaries as well as the margin depth. We believe this method can serve as a useful adjunct in glioma surgery, with further studies necessary for real-world practical applications.

The Role of Genetic Analysis in Distinguishing Multifocal and Multicentric Glioblastomas: An Illustrative Case

Introduction

Glioblastomas can manifest as multiple, simultaneous, noncontiguous lesions. We genetically analyzed multiple glioblastomas and discuss their etiological origins in this report.

Case Presentation

We present the case of a 47-year-old woman who presented with memory impairment and left partial paralysis. Radiographic imaging revealed three apparently noncontiguous lesions in the right temporal and parietal lobes extending into the corpus callosum, leading to diagnosis of multicentric glioblastomas. All three lesions were excised. Genetic analysis of the lesions revealed a TERT promoter C228T mutation, a roughly equivalent amplification of EGFR, and homozygous deletion of CDKN2A/B exclusively in the two contrast-enhanced lesions. Additionally, the contrast-enhanced lesions exhibited the same two-base pair mutations of PTEN, whereas the non-enhanced lesion showed a partially distinct 13-base pair mutation. The other genetic characteristics were consistent. Rather than each having arisen de novo, we believe that they had developed by infiltration and are therefore best classified as multifocal glioblastomas.

Conclusion

Our findings underscore anew the possibility of infiltration by glioblastomas, even within regions devoid of signal alterations on T2-weighted images or fluid-attenuated inversion recovery images. Genetic analysis can play a crucial role in differentiating whether multiple glioblastomas are multifocal or multicentric.

Predictors for delayed awakening in adult glioma patients receiving awake craniotomy under monitored anesthesia care

PURPOSE

Delayed awakening after anesthetic discontinuation during awake craniotomy is associated with somnolence during functional brain mapping. However, predictors of delayed awakening in patients receiving monitored anesthesia care for awake craniotomy are unknown.

METHODS

This retrospective cohort study analyzed 117 adult patients with supratentorial glioma in or near eloquent areas who received monitored anesthesia care for awake craniotomy between July 2020 and January 2023 at Linkou Chang Gung Memorial Hospital. These patients were divided into two groups according to their time to awakening (ability to speak their names) after propofol cessation: longer or shorter than 20 min (median duration). Because propofol was solely used anesthetic from skin incision to dural opening, parameters in Schnider model for propofol target-controlled infusion, such as age, sex, and BMI, were adjusted or propensity-matched to compare their anesthetic, surgical, and histopathological profiles.

RESULTS

After propensity-matched comparisons of age and BMI, significant predictors of delayed awakening included IDH1 wild-type tumors and repeated craniotomies. Subgroup analysis revealed that older age and larger T2 volume were predictors in patients undergoing the first craniotomy, while lower preoperative Karnofsky performance scale scores and depression were predictors in repeated craniotomy cases. Delayed awakening was also associated with somnolence and a lower gross total resection rate.

CONCLUSION

Our retrospective analysis of patients receiving monitored anesthesia care for awake craniotomy revealed that delayed awakening after propofol discontinuation occurred more often in patients with IDH1 wild-type tumors and repeated craniotomies. Also, delayed awakening was associated with somnolence during functional mapping and a lower gross total resection rate.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI)

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

Technical Aspects of Motor and Language Mapping in Glioma Patients

Gliomas are infiltrative primary brain tumors that often invade functional cortical and subcortical regions, and they mandate individualized brain mapping strategies to avoid postoperative neurological deficits. It is well known that maximal safe resection significantly improves survival, while postoperative deficits minimize the benefits associated with aggressive resections and diminish patients’ quality of life. Although non-invasive imaging tools serve as useful adjuncts, intraoperative stimulation mapping (ISM) is the gold standard for identifying functional cortical and subcortical regions and minimizing morbidity during these challenging resections. Current mapping methods rely on the use of low-frequency and high-frequency stimulation, delivered with monopolar or bipolar probes either directly to the cortical surface or to the subcortical white matter structures. Stimulation effects can be monitored through patient responses during awake mapping procedures and/or with motor-evoked and somatosensory-evoked potentials in patients who are asleep. Depending on the patient’s preoperative status and tumor location and size, neurosurgeons may choose to employ these mapping methods during awake or asleep craniotomies, both of which have their own benefits and challenges. Regardless of which method is used, the goal of intraoperative stimulation is to identify areas of non-functional tissue that can be safely removed to facilitate an approach trajectory to the equator, or center, of the tumor. Recent technological advances have improved ISM’s utility in identifying subcortical structures and minimized the seizure risk associated with cortical stimulation. In this review, we summarize the salient technical aspects of which neurosurgeons should be aware in order to implement intraoperative stimulation mapping effectively and safely during glioma surgery.

The combination of radiomics features and VASARI standard to predict glioma grade

Background and Purpose

Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.

Materials and Methods

Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.

Results

Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.

Conclusion

The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.