A Network-Based Approach to Glioma Surgery: Insights from Functional Neurosurgery

Samuel, Nardin; Vetkas, Artur; Pancholi, Aditya; Sarica, Can; Loh, Aaron; Germann, Jürgen; Harmsen, Irene E.; Tasserie, Jordy; Milano, Vanessa; Yamamoto, Kazuaki; Kalia, Suneil K.; Kongkham, Paul; Lozano, Andrés M.

doi:10.3390/cancers13236127

Cited by 11 publications

(4 citation statements)

References 65 publications

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“… 36–38 Nevertheless, initial studies in glioma patients suggested that evaluating brain networks may help further elucidate the cognitive decline of various domains. 13–15 , 39 , 40 In the present study, we hypothesized that decreased structural connectivity in whole-brain networks is associated with cognitive deterioration in glioma patients. Therefore, we applied an innovative tractography algorithm 4 , 23 in combination with a network-based parcellation that allowed the construction of a whole-brain connectome of the structurally altered brain of pretreated glioma patients in conjunction with a well-developed method for predicting traits and symptoms from connectivity data.…”

Section: Discussionmentioning

confidence: 93%

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

Friedrich,

Filss,

Lohmann

et al. 2023

Neuro-Oncology Advances

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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 ECOG score 1; CNS-WHO grade 3 or 4) after multimodal therapy. Cognitive performance was assessed by ten tests in five 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 nine cognitive tests. The models predicted the scores of 7/10 tests (median correlation coefficient, 0.47; range, 0.39-0.57) from 0.6-5.4% 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.

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Section: Discussionmentioning

confidence: 93%

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

Friedrich,

Filss,

Lohmann

et al. 2023

Neuro-Oncology Advances

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“…In traditional glioma surgery, lesion localization primarily depends upon the neurosurgeon’s understanding and experience with imaging data. After craniotomy, the brain sulcus depth, gyrus shape, color and blood vessel flow are observed and estimated ( 1 , 47 , 48 ). The above indications cannot be used to accurately evaluate the entire lesion contour.…”

Section: Discussionmentioning

confidence: 99%

Progress in the application of ultrasound in glioma surgery

Wu,

Cheng,

Gao

et al. 2024

Front. Med.

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Brain glioma, which is highly invasive and has a poor prognosis, is the most common primary intracranial tumor. Several studies have verified that the extent of resection is a considerable prognostic factor for achieving the best results in neurosurgical oncology. To obtain gross total resection (GTR), neurosurgery relies heavily on generating continuous, real-time, intraoperative glioma descriptions based on image guidance. Given the limitations of existing devices, it is imperative to develop a real-time image-guided resection technique to offer reliable functional and anatomical information during surgery. At present, the application of intraoperative ultrasound (IOUS) has been indicated to enhance resection rates and maximize brain function preservation. IOUS, which is promising due to its lower cost, minimal operational flow interruptions, and lack of radiation exposure, can enable real-time localization and precise tumor size and form descriptions while assisting in discriminating residual tumors and solving brain tissue shifts. Moreover, the application of new advancements in ultrasound technology, such as contrast-enhanced ultrasound (CEUS), three-dimensional ultrasound (3DUS), noninvasive ultrasound (NUS), and ultrasound elastography (UE), could assist in achieving GTR in glioma surgery. This article reviews the advantages and disadvantages of IOUS in glioma surgery.

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“…Tumor growth patterns could be further assessed with large-scale data using normative structural connectomics. Two recently published reviews also suggest the application of brain connectomics for glioma surgery [50,51]. Similarly, understanding tumor behaviors and associated symptoms might require a whole-brain approach [52].…”

Section: Neuro-oncology Applicationsmentioning

confidence: 99%

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

Germann

Zadeh

Mansouri

et al. 2022

Cancers

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Neuro-oncology research is broad and includes several branches, one of which is neuroimaging. Magnetic resonance imaging (MRI) is instrumental for the diagnosis and treatment monitoring of patients with brain tumors. Most commonly, structural and perfusion MRI sequences are acquired to characterize tumors and understand their behaviors. Thanks to technological advances, structural brain MRI can now be transformed into a so-called average brain accounting for individual morphological differences, which enables retrospective group analysis. These normative analyses are uncommonly used in neuro-oncology research. Once the data have been normalized, voxel-wise analyses and spatial mapping can be performed. Additionally, investigations of underlying connectomics can be performed using functional and structural templates. Additionally, a recently available template of spatial transcriptomics has enabled the assessment of associated gene expression. The few published normative analyses have shown relationships between tumor characteristics and spatial localization, as well as insights into the circuitry associated with epileptogenic tumors and depression after cingulate tumor resection. The wide breadth of possibilities with normative analyses remain largely unexplored, specifically in terms of connectomics and imaging transcriptomics. We provide a framework for performing normative analyses in oncology while also highlighting their limitations. Normative analyses are an opportunity to address neuro-oncology questions from a different perspective.

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A Network-Based Approach to Glioma Surgery: Insights from Functional Neurosurgery

Cited by 11 publications

References 65 publications

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

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

Progress in the application of ultrasound in glioma surgery

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

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