Network-level prediction of set-shifting deterioration after lower-grade glioma resection

Predictors of cognition after glioma surgery: connectotomy, structure-function phenotype, plasticity

Determining preoperatively the maximal extent of resection that would preserve cognitive functions is the core challenge of brain tumour surgery. Over the past decade, the methodological framework to achieve this goal has been thoroughly renewed: the population-level topographically-focused voxel-based lesion-symptom mapping has been progressively overshadowed by machine learning (ML) algorithmics, in which the problem is framed as predicting cognitive outcomes in a patient-specific manner from a typically large set of variables. However, the choice of these predictors is of utmost importance, as they should be both informative and parsimonious. In this perspective, we first introduce the concept of connectotomy: instead of parameterizing resection topography through the status (intact/resected) of a huge number of voxels (or parcels) paving the whole brain in the Cartesian 3D-space, the connectotomy models the resection in the connectivity space, by computing a handful number of networks disconnection indices, measuring how the structural connectivity sustaining each network of interest was hit by the resection. This connectivity-informed reduction of dimensionality is a necessary step for efficiently implementing ML tools, given the relatively small number of patient-examples in available training datasets. We further argue that two other major sources of interindividual variability must be considered to improve the accuracy with which outcomes are predicted: the underlying structure-function phenotype and neuroplasticity, for which we provide an in-depth review and propose new ways of determining relevant predictors. We finally discuss the benefits of our approach for precision surgery of glioma.

Structural connectome-based predictive modeling of cognitive deficits in treated glioma patients

Background

In glioma patients, tumor growth and subsequent treatments are associated with various types of brain lesions. We hypothesized that cognitive functioning in these patients critically depends on the maintained structural connectivity of multiple brain networks.

Methods

The study included 121 glioma patients (median age, 52 years; median Eastern Cooperative Oncology Group performance score 1; CNS-WHO Grade 3 or 4) after multimodal therapy. Cognitive performance was assessed by 10 tests in 5 cognitive domains at a median of 14 months after treatment initiation. Hybrid amino acid PET/MRI using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine, a network-based cortical parcellation, and advanced tractography were used to generate whole-brain fiber count-weighted connectivity matrices. The matrices were applied to a cross-validated machine-learning model to identify predictive fiber connections (edges), critical cortical regions (nodes), and the networks underlying cognitive performance.

Results

Compared to healthy controls (n = 121), patients' cognitive scores were significantly lower in 9 cognitive tests. The models predicted the scores of 7/10 tests (median correlation coefficient, 0.47; range, 0.39-0.57) from 0.6% to 5.4% of the matrix entries; 84% of the predictive edges were between nodes of different networks. Critically involved cortical regions (≥10 adjacent edges) included predominantly left-sided nodes of the visual, somatomotor, dorsal/ventral attention, and default mode networks. Highly critical nodes (≥15 edges) included the default mode network's left temporal and bilateral posterior cingulate cortex.

Conclusions

These results suggest that the cognitive performance of pretreated glioma patients is strongly related to structural connectivity between multiple brain networks and depends on the integrity of known network hubs also involved in other neurological disorders.

Dynamics in cognition and health-related quality of life in grade 2 and 3 gliomas after surgery

BACKGROUND

The focus of clinical management and research in gliomas has been on survival, but the interest in the treatment effects on cognition and health-related quality of life (HRQoL) is emerging. The primary aim of this study was to investigate the dynamics in cognition after brain tumor surgery for astrocytomas and oligodendrogliomas grade 2 and 3. The secondary aim was to investigate the association of postoperative changes in cognition with changes HRQoL.

METHODS

In this observational study, 48 patients operated for an astrocytoma or oligodendrogliomas, grade 2 or 3, at the Department of Neurosurgery, Uppsala, Sweden, 2016-2021, were included. Cognitive and language skills were assessed with a selected test battery and HRQoL was patient-reported as assessed with RAND-36 pre- and approximately 3 months postoperatively.

RESULTS

There was a significant postoperative decrease in attention span and verbal learning, but the patients improved in the test for visual memory. There was no change in visual attention, executive function, verbal memory, visual organization and construction, verbal fluency, and confrontation naming. The RAND-36 variables physical function, role physical, general health, vitality, and social functioning decreased significantly after surgery. Patients operated for tumor recurrence exhibited greater deterioration in attention and a greater extent of resection correlated with a less pronounced decrease in verbal memory, but there were otherwise weak associations between the dynamics in cognition and patient-, tumor-, and treatment-variables. A decline in cognitive variables was not associated with worse HRQoL.

CONCLUSIONS

Although both several cognitive and HRQoL domains deteriorated postoperatively, these changes did not correlate with each other. This highlights the complexity of cognitive and HRQoL dynamics in the early postoperative phase.

Incidence of ischemic complications and technical nuances of arteries preservation for insular gliomas resection

Introduction

Insular gliomas have complex anatomy and microvascular supply that make resection difficult. Furthermore, resection of insular glioma is associated with a significant risk of postoperative ischemic complications. Thus, this study aimed to assess the incidence of ischemic complications related to insular glioma resection, determine its risk factors, and describe a single surgeon's experience of artery-preserving tumor resection.

Methods

We enrolled 75 consecutive patients with insular gliomas who underwent transcortical tumor resection. Preoperative and postoperative demographic, clinical, radiological [including diffusion-weighted imaging (DWI)], intraoperative neurophysiological data, and functional outcomes were analyzed. Motor evoked potentials (MEPs) and radiological characteristics like the relationship between the proximal segment of the lateral lenticulostriate arteries (LLSAs) and the tumor, the flat inner edge sign (the inner edge of the insular glioma is well-defined) or obscure inner edge sign, the distance between the lesion and posterior limb of the internal capsule and the invasion of the superior limiting sulcus by the tumor were analyzed. Strategies such as "residual triangle," "basal ganglia outline reappearance," and "sculpting" technique were used to preserve the LLSAs and the main branches of M2 for maximal tumor resection according to the Berger-Sinai classification.

Results

Postoperative DWI showed acute ischemia in 44 patients (58.7%). Moreover, nine patients (12%) had developed new motor deficits, as determined by the treating neurosurgeons. The flat inner edge sign [odds ratio (OR), 0.144; 95% confidence interval (CI), 0.024-0.876) and MEPs (>50%) (OR, 18.182; 95% CI, 3.311-100.00) were significantly associated with postoperative core ischemia, which affected the posterior limb of the internal capsule or corona radiata.

Conclusions

Insular glioma resection was associated with a high incidence of ischemia, as detected by DWI, as well as new motor deficits that were determined by the treating neurosurgeons. Insular glioma patients with obscure inner edge signs and intraoperative MEPs decline >50% had a higher risk of developing core ischemia. With our strategies, maximal safe resection of insular gliomas may be achieved.

A Personalized Longitudinal Strategy in Low-Grade Glioma Patients: Predicting Oncological and Neural Interindividual Variability and Its Changes over Years to Think One Step Ahead

Diffuse low-grade glioma (LGG) is a rare cerebral cancer, mostly involving young adults with an active life at diagnosis. If left untreated, LGG widely invades the brain and becomes malignant, generating neurological worsening and ultimately death. Early and repeat treatments for this incurable tumor, including maximal connectome-based surgical resection(s) in awake patients, enable postponement of malignant transformation while preserving quality of life owing to constant neural network reconfiguration. Due to considerable interindividual variability in terms of LGG course and consecutive cerebral reorganization, a multistage longitudinal strategy should be tailored accordingly in each patient. It is crucial to predict how the glioma will progress (changes in growth rate and pattern of migration, genetic mutation, etc.) and how the brain will adapt (changes in patterns of spatiotemporal redistribution, possible functional consequences such as epilepsy or cognitive decline, etc.). The goal is to anticipate therapeutic management, remaining one step ahead in order to select the optimal (re-)treatment(s) (some of them possibly kept in reserve), at the appropriate time(s) in the evolution of this chronic disease, before malignization and clinical worsening. Here, predictive tumoral and non-tumoral factors, and their ever-changing interactions, are reviewed to guide individual decisions in advance based on patient-specific markers, for the treatment of LGG.