Functional Mapping for Glioma Surgery, Part 1: Preoperative Mapping Tools

Toward a systematic grading for the selection of patients to undergo awake surgery: identifying suitable predictor variables

Background

Awake craniotomy is the standard of care for treating language eloquent gliomas. However, depending on preoperative functionality, it is not feasible in each patient and selection criteria are highly heterogeneous. Thus, this study aimed to identify broadly applicable predictor variables allowing for a more systematic and objective patient selection.

Methods

We performed post-hoc analyses of preoperative language status, patient and tumor characteristics including language eloquence of 96 glioma patients treated in a single neurosurgical center between 05/2018 and 01/2021. Multinomial logistic regression and stepwise variable selection were applied to identify significant predictors of awake surgery feasibility.

Results

Stepwise backward selection confirmed that a higher number of paraphasias, lower age, and high language eloquence level were suitable indicators for an awake surgery in our cohort. Subsequent descriptive and ROC-analyses indicated a cut-off at ≤54 years and a language eloquence level of at least 6 for awake surgeries, which require further validation. A high language eloquence, lower age, preexisting semantic and phonological aphasic symptoms have shown to be suitable predictors.

Conclusion

The combination of these factors may act as a basis for a systematic and standardized grading of patients' suitability for an awake craniotomy which is easily integrable into the preoperative workflow across neurosurgical centers.

Transcranial Magnetic Stimulation-Based Machine Learning Prediction of Tumor Grading in Motor-Eloquent Gliomas

BACKGROUND

Navigated transcranial magnetic stimulation (nTMS) is a well-established preoperative mapping tool for motor-eloquent glioma surgery. Machine learning (ML) and nTMS may improve clinical outcome prediction and histological correlation.

METHODS

This was a retrospective cohort study of patients who underwent surgery for motor-eloquent gliomas between 2018 and 2022. Ten healthy subjects were included. Preoperative nTMS-derived variables were collected: resting motor threshold (RMT), interhemispheric RMT ratio (iRMTr)-abnormal if above 10%-and cortical excitability score-number of abnormal iRMTrs. World Health Organization (WHO) grade and molecular profile were collected to characterize each tumor. ML models were fitted to the data after statistical feature selection to predict tumor grade.

RESULTS

A total of 177 patients were recruited: WHO grade 2-32 patients, WHO grade 3-65 patients, and WHO grade 4-80 patients. For the upper limb, abnormal iRMTr were identified in 22.7% of WHO grade 2, 62.5% of WHO grade 3, and 75.4% of WHO grade 4 patients. For the lower limb, iRMTr was abnormal in 23.1% of WHO grade 2, 67.6% of WHO grade 3%, and 63.6% of WHO grade 4 patients. Cortical excitability score (P = .04) was statistically significantly related with WHO grading. Using these variables as predictors, the ML model had an accuracy of 0.57 to predict WHO grade 4 lesions. In subgroup analysis of high-grade gliomas vs low-grade gliomas, the accuracy for high-grade gliomas prediction increased to 0.83. The inclusion of molecular data into the model-IDH mutation and 1p19q codeletion status-increases the accuracy of the model in predicting tumor grading (0.95 and 0.74, respectively).

CONCLUSION

ML algorithms based on nTMS-derived interhemispheric excitability assessment provide accurate predictions of HGGs affecting the motor pathway. Their accuracy is further increased when molecular data are fitted onto the model paving the way for a joint preoperative approach with radiogenomics.

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.

Comparison of anatomical-based vs. nTMS-based risk stratification model for predicting postoperative motor outcome and extent of resection in brain tumor surgery

BACKGROUND

Two statistical models have been established to evaluate characteristics associated with postoperative motor outcome in patients with glioma associated to the motor cortex (M1) or the corticospinal tract (CST). One model is based on a clinicoradiological prognostic sum score (PrS) while the other one relies on navigated transcranial magnetic stimulation (nTMS) and diffusion-tensor-imaging (DTI) tractography. The objective was to compare the models regarding their prognostic value for postoperative motor outcome and extent of resection (EOR) with the aim of developing a combined, improved model.

METHODS

We retrospectively analyzed a consecutive prospective cohort of patients who underwent resection for motor associated glioma between 2008 and 2020, and received a preoperative nTMS motor mapping with nTMS-based diffusion tensor imaging tractography. The primary outcomes were the EOR and the motor outcome (on the day of discharge and 3 months postoperatively according to the British Medical Research Council (BMRC) grading). For the nTMS model, the infiltration of M1, tumor-tract distance (TTD), resting motor threshold (RMT) and fractional anisotropy (FA) were assesed. For the PrS score (ranging from 1 to 8, lower scores indicating a higher risk), we assessed tumor margins, volume, presence of cysts, contrast agent enhancement, MRI index (grading white matter infiltration), preoperative seizures or sensorimotor deficits.

RESULTS

Two hundred and three patients with a median age of 50 years (range: 20-81 years) were analyzed of whom 145 patients (71.4%) received a GTR. The rate of transient new motor deficits was 24.1% and of permanent new motor deficits 18.8%. The nTMS model demonstrated a good discrimination ability for the short-term motor outcome at day 7 of discharge (AUC = 0.79, 95 %CI: 0.72-0.86) and the long-term motor outcome after 3 months (AUC = 0.79, 95 %CI: 0.71-0.87). The PrS score was not capable to predict the postoperative motor outcome in this cohort but was moderately associated with the EOR (AUC = 0.64; CI 0.55-0.72). An improved, combined model was calculated to predict the EOR more accurately (AUC = 0.74, 95 %CI: 0.65-0.83).

CONCLUSION

The nTMS model was superior to the clinicoradiological PrS model for potentially predicting the motor outcome. A combined, improved model was calculated to estimate the EOR. Thus, patient counseling and surgical planning in patients with motor-associated tumors should be performed using functional nTMS data combined with tractography.

Preoperative Navigated Transcranial Magnetic Stimulation: New Insight for Brain Tumor-Related Language Mapping

Preoperative brain mapping methods are particularly important in modern neuro-oncology when a tumor affects eloquent language areas since damage to parts of the language circuits can cause significant impairments in daily life. This narrative review examines the literature regarding preoperative and intraoperative language mapping using repetitive navigated transcranial magnetic stimulation (rnTMS) with or without direct electrical stimulation (DES) in adult patients with tumors in eloquent language areas. The literature shows that rnTMS is accurate in detecting preexisting language disorders and positive intraoperative mapping regions. In terms of the region extent and clinical outcomes, rnTMS has been shown to be accurate in identifying positive sites to guide resection, reducing surgery duration and craniotomy size and thus improving clinical outcomes. Before incorporating rnTMS into the neurosurgical workflow, the refinement of protocols and a consensus within the neuro-oncology community are required.

Preoperative function-specific connectome analysis predicts surgery-related aphasia after glioma resection

Glioma resection within language-eloquent regions poses a high risk of surgery-related aphasia (SRA). Preoperative functional mapping by navigated transcranial magnetic stimulation (nTMS) combined with diffusion tensor imaging (DTI) is increasingly used to localize cortical and subcortical language-eloquent areas. This study enrolled 60 nonaphasic patients with left hemispheric perisylvian gliomas to investigate the prediction of SRA based on function-specific connectome network properties under different fractional anisotropy (FA) thresholds. Moreover, we applied a machine learning model for training and cross-validation to predict SRA based on preoperative connectome parameters. Preoperative connectome analysis helps predict SRA development with an accuracy of 73.3% and sensitivity of 78.3%. The current study provides a new perspective of combining nTMS and function-specific connectome analysis applied in a machine learning model to investigate language in neurooncological patients and promises to advance our understanding of the intricate networks.

Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.

Multimodal integrated approaches in low grade glioma surgery

Surgical management of Diffuse Low-Grade Gliomas (DLGGs) has radically changed in the last 20 years. Awake surgery (AS) in combination with Direct Electrical Stimulation (DES) and real-time neuropsychological testing (RTNT) permits continuous intraoperative feedback, thus allowing to increase the extent of resection (EOR). The aim of this study was to evaluate the impact of the technological advancements and integration of multidisciplinary techniques on EOR. Two hundred and eighty-eight patients affected by DLGG were enrolled. Cases were stratified according to the surgical protocol that changed over time: 1. DES; 2. DES plus functional MRI/DTI images fused on a NeuroNavigation system; 3. Protocol 2 plus RTNT. Patients belonging to Protocol 1 had a median EOR of 83% (28–100), while those belonging to Protocol 2 and 3 had a median EOR of 88% (34–100) and 98% (50–100) respectively (p = 0.0001). New transient deficits with Protocol 1, 2 and 3 were noted in 38.96%, 34.31% and 31,08% of cases, and permanent deficits in 6.49%, 3.65% and 2.7% respectively. The average follow-up period was 6.8 years. OS was influenced by molecular class (p = 0.028), EOR (p = 0.018) and preoperative tumor growing pattern (p = 0.004). Multimodal surgical approach can provide a safer and wider removal of DLGG with potential subsequent benefits on OS. Further studies are necessary to corroborate our findings.