Anatomical features of primary brain tumors affect seizure risk and semiology

Mapping Lesion-Related Epilepsy to a Human Brain Network

Importance

It remains unclear why lesions in some locations cause epilepsy while others do not. Identifying the brain regions or networks associated with epilepsy by mapping these lesions could inform prognosis and guide interventions.

Objective

To assess whether lesion locations associated with epilepsy map to specific brain regions and networks.

Design, Setting, and Participants

This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded.

Main Outcomes and Measures

Epilepsy or no epilepsy.

Results

Lesion locations from 76 patients with poststroke epilepsy (39 [51%] male; mean [SD] age, 61.0 [14.6] years; mean [SD] follow-up, 6.7 [2.0] years) and 625 control patients with stroke (366 [59%] male; mean [SD] age, 62.0 [14.1] years; follow-up range, 3-12 months) were included in the discovery data set. Lesions associated with epilepsy occurred in multiple heterogenous locations spanning different lobes and vascular territories. However, these same lesion locations were part of a specific brain network defined by functional connectivity to the basal ganglia and cerebellum. Findings were validated in 4 independent cohorts including 772 patients with brain lesions (271 [35%] with epilepsy; 515 [67%] male; median [IQR] age, 60 [50-70] years; follow-up range, 3-35 years). Lesion connectivity to this brain network was associated with increased risk of epilepsy after stroke (odds ratio [OR], 2.82; 95% CI, 2.02-4.10; P < .001) and across different lesion types (OR, 2.85; 95% CI, 2.23-3.69; P < .001). Deep brain stimulation site connectivity to this same network was associated with improved seizure control (r, 0.63; P < .001) in 30 patients with drug-resistant epilepsy (21 [70%] male; median [IQR] age, 39 [32-46] years; median [IQR] follow-up, 24 [16-30] months).

Conclusions and Relevance

The findings in this study indicate that lesion-related epilepsy mapped to a human brain network, which could help identify patients at risk of epilepsy after a brain lesion and guide brain stimulation therapies.

Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas

Purpose

To investigate the association between clinic-radiological features and glioma-associated epilepsy (GAE), we developed and validated a radiomics nomogram for predicting GAE in WHO grade II~IV gliomas.

Methods

This retrospective study consecutively enrolled 380 adult patients with glioma (266 in the training cohort and 114 in the testing cohort). Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. The semantic radiological characteristics were assessed by a radiologist with 15 years of experience in neuro-oncology. A clinic-radiological model, radiomic signature, and a combined model were built for predicting GAE. The combined model was visualized as a radiomics nomogram. The AUC was used to evaluate model classification performance, and the McNemar test and Delong test were used to compare the performance among the models. Statistical analysis was performed using SPSS software, and p < 0.05 was regarded as statistically significant.

Results

The combined model reached the highest AUC with the testing cohort (training cohort, 0.911 [95% CI, 0.878-0.942]; testing cohort, 0.866 [95% CI, 0.790-0.929]). The McNemar test revealed that the differences among the accuracies of the clinic-radiological model, radiomic signature, and combined model in predicting GAE in the testing cohorts (p > 0.05) were not significantly different. The DeLong tests showed that the difference between the performance of the radiomic signature and the combined model was significant (p < 0.05).

Conclusion

The radiomics nomogram predicted seizures in patients with glioma non-invasively, simply, and practically. Compared with the radiomics models, comprehensive clinic-radiological imaging signs observed by the naked eye have non-discriminatory performance in predicting GAE.

Utility of Epilepsy Surgery in Survivors of Childhood Cancer

Resection of an epileptogenic focus improves seizure control in patients with drug-resistant epilepsy. There is little data available on usefulness of epilepsy surgery in childhood cancer survivors with drug-resistant epilepsy. To learn about seizure outcome after epilepsy surgery in childhood cancer survivors, we retrospectively reviewed charts of 42 children who were referred to an epilepsy center for surgical evaluation. Sixteen children (38%) were offered epilepsy surgery and 10 consented. Seizure outcome was classified based on International League Against Epilepsy outcome scale. All 10 children were having multiple seizures a month on therapeutic doses of three antiepilepsy drugs (AEDs). At a median follow-up of 5.6 years after epilepsy surgery, three children had class 1 outcome (no seizures), four had class 3 outcome (1-3 seizure days/year), and three had class 4 outcome (≥ 50% reduction in seizure frequency). One child was off AEDs, seven were on a single AED, and two were on three AEDs at their last follow-up. Epilepsy surgery had low morbidity and improved seizure control in childhood cancer survivors with drug-resistant epilepsy. Childhood cancer survivors with drug-resistant epilepsy should be referred to an epilepsy center for a higher level of care.

Potential surgical therapies for drug-resistant focal epilepsy

Drug-resistant focal epilepsy (DRFE), defined by failure of two antiepileptic drugs, affects 30% of epileptic patients. Epilepsy surgeries are alternative options for this population. Preoperative evaluation is critical to include potential candidates, and to choose the most appropriate procedure to maximize efficacy and simultaneously minimize side effects. Traditional procedures involve open skull surgeries and epileptic focus resection. Alternatively, neuromodulation surgeries use peripheral nerve or deep brain stimulation to reduce the activities of epileptogenic focus. With the advanced improvement of laser-induced thermal therapy (LITT) technique and its utilization in neurosurgery, magnetic resonance-guided LITT (MRgLITT) emerges as a minimal invasive approach for drug-resistant focal epilepsy. In the present review, we first introduce drug-resistant focal epilepsy and summarize the indications, pros and cons of traditional surgical procedures and neuromodulation procedures. And then, focusing on MRgLITT, we thoroughly discuss its history, its technical details, its safety issues, and current evidence on its clinical applications. A case report on MRgLITT is also included to illustrate the preoperational evaluation. We believe that MRgLITT is a promising approach in selected patients with drug-resistant focal epilepsy, although large prospective studies are required to evaluate its efficacy and side effects, as well as to implement a standardized protocol for its application.

Topographic volume-standardization atlas of the human brain

Specific anatomical patterns are seen in various diseases affecting the brain. Clinical studies on the topography of pathologies are often limited by the absence of a normalization of the prevalence of pathologies to the relative volume of the affected anatomical structures. A comprehensive reference on the relative volumes of clinically relevant anatomical structures serving for such a normalization, is currently lacking. The analyses are based on anatomical high-resolution three-dimensional T1-weighted magnetic resonance imaging data of 30 healthy Caucasian volunteers, including 14 females (mean age 37.79 years, SD 13.04) and 16 males (mean age 38.31 years, SD 16.91). Semi-automated anatomical segmentation was used, guided by a neuroanatomical parcellation algorithm differentiating 96 structures. Relative volumes were derived by normalizing parenchymal structures to the total individual encephalic volume and ventricular segments to the total individual ventricular volume. The present investigation provides the absolute and relative volumes of 96 anatomical parcellation units of the human encephalon. A larger absolute volume in males than in females is found for almost all parcellation units. While parenchymal structures display a trend towards decreasing volumes with increasing age, a significant inverse effect is seen with the ventricular system. The variances in volumes as well as the effects of gender and age are given for each structure before and after normalization. The provided atlas constitutes an anatomically detailed and comprehensive analysis of the absolute and relative volumes of the human encephalic structures using a clinically oriented parcellation algorithm. It is intended to serve as a reference for volume-standardization in clinical studies on the topographic prevalence of pathologies.

Safety of the paramedian supracerebellar-transtentorial approach for selective amygdalohippocampectomy

OBJECTIVE

The goal of this study was to assess the reproducibility and safety of the recently introduced paramedian supracerebellar-transtentorial (PST) approach for selective amygdalohippocampectomy (SA).

METHODS

The authors performed a retrospective analysis of prospectively collected data originating from their surgical register of patients undergoing SA via a PST approach for lesional medial temporal lobe epilepsy. All patients received thorough pre- and postoperative clinical (neurological, neuropsychological, psychiatric) and instrumental (ictal and long-term EEG, invasive EEG if needed, MRI) workup. Surgery-induced complications were assessed at discharge and at every follow-up thereafter and were classified according to Clavien-Dindo grade (CDG). Epilepsy outcome was defined according to Engel classification. Data were reported according to common descriptive statistical methods.

RESULTS

Between May 2015 and May 2018, 17 patients underwent SA via a PST approach at the authors' institution (hippocampal sclerosis in 13 cases, WHO grade II glioma in 2 cases, and reactive gliosis in 2 cases). The median postoperative follow-up was 7 months (mean 9 months, range 3-19 months). There was no surgery-related mortality and no complication (CDG ≥ 2) in the whole series. Transitory CDG 1 surgical complications occurred in 4 patients and had resolved in all of them by the first postoperative follow-up. One patient showed a deterioration of neuropsychological performance with new slight mnestic deficits. No patient experienced a clinically relevant postoperative visual field defect. No morbidity due to semisitting position was recorded. At last follow-up 13/17 (76.4%) patients were in Engel class I (9/17 [52.9%] were in class IA).

CONCLUSIONS

The PST approach is a reproducible and safe surgical route for SA. The achievable complication rate is in line with the best results in the literature. Visual function outcome particularly benefits from this highly selective, neocortex-sparing approach. A larger patient sample and longer follow-up will show in the future if the seizure control rate and neuropsychological outcome also compare better than those achieved with current common surgical techniques.

Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction

OBJECTIVE

The aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy.

METHODS

Clinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated.

RESULTS

A total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66).

CONCLUSIONS

This study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency.

Distinct topographic-anatomical patterns in primary and secondary brain tumors and their therapeutic potential

PURPOSE

Understanding the topographic-anatomical patterns of brain tumors has the potential to improve our pathophysiological understanding and may allow for anatomical tailoring of surgery and radiotherapy. This study analyzed topographic-anatomical patterns underlying neuroepithelial tumors, primary CNS lymphoma and metastases.

METHODS

Any histologically confirmed supra- or infratentorial parenchymal neoplasia of one institution over a 4-year period was included. Using high-resolution magnetic resonance imaging data, a detailed analysis of the topographic-anatomical tumor features was performed. Differences between neuroepithelial tumors, primary central nervous system lymphoma (PCNSL) and metastases were assessed using pairwise comparisons adjusted for multiple testing, upon significance of the omnibus test.

RESULTS

Based on image analysis of 648 patients-419 (65%) neuroepithelial tumors, 28 (5%) PCNSL and 201 (31%) metastases-entity-specific topographic-anatomical patterns were identified. Neuroepithelial tumors showed a radial ventriculo-cortical orientation, inconsistent with the current belief of a growth along white matter tracts, whereas the pattern in PCNSL corresponded to a growth along such. Metastases preferentially affected the cortex and subcortical white matter of large arteries' terminal supply areas. This study provides a comprehensive anatomical description of the topography of NT, PCNSL and metastases intended to serve as a topographic reference for clinicians and neuroscientists.

CONCLUSIONS

The identified distinct anatomical patterns provide evidence for a specific interaction between tumor and anatomical structures, following a pathoclitic concept. Understanding differences in their anatomical behavior has the potential to improve our pathophysiological understanding and to tailor therapy of brain tumors.

Biomarkers of Seizure Activity in Patients With Intracranial Metastases and Gliomas: A Wide Range Study of Correlated Regions of Interest

Introduction: Studies quantifying cortical metrics in brain tumor patients who present with seizures are limited. The current investigation assesses morphometric/volumetric differences across a wide range of anatomical regions, including temporal and extra-temporal, in patients with gliomas and intracranial metastases (IMs) presenting with seizures that could serve as a biomarker in the identification of seizure expression and serve as a neuronal target for mitigation.

Methods: In a retrospective design, the MR sequences of ninety-two tumor patients [55% gliomas; 45% IM] and 34 controls were subjected to sophisticated morphometric and volumetric assessments using BrainSuite and MATLAB modules. We examined 103 regions of interests (ROIs) across eight distinct cortical categories of interests (COI) [gray matter, white matter; total volume, CSF; cortical areas: inner, mid, pial; cortical thickness]. The primary endpoint was quantifying and identifying ROIs with significant differences in z-scores based upon the presence of seizures. Feature selection employing neighborhood component analysis (NCA) determined the ROI within each COI having the highest significance/weight in the differentiation of seizure vs. non-seizure patients harboring brain tumor.

Results: Overall, the mean age of the cohort was 58.0 ± 12.8 years, and 45% were women. The prevalence of seizures in tumor patients was 28%. Forty-two ROIs across the eight pre-defined COIs had significant differences in z-scores between tumor patients presenting with and without seizures. The NCA feature selection noted the volume of pars-orbitalis and right middle temporal gyrus to have the highest weight in differentiating tumor patients based on seizures for three distinct COIs [GM, total volume, and CSF volume] and white matter, respectively. Left-sided transverse temporal gyrus, left precuneus, left transverse temporal, and left supramarginal gyrus were associated with having the highest weight in the differentiation of seizure vs. non-seizure in tumor patients for morphometrics relating to cortical areas in the pial, inner and mid regions and cortical thickness, respectively.

Conclusion: Our study elucidates potential biomarkers for seizure targeting in patients with gliomas and IMs based upon morphometric and volumetric assessments. Amongst the widespread brain regions examined in our cohort, pars orbitalis, supramarginal and temporal gyrus (middle, transverse), and the pre-cuneus contribute a maximal potential for differentiation of seizure patients from non-seizure.

Seizures in patients with cancer

Seizures are common in patients with cancer and either result from brain lesions, paraneoplastic syndromes, and complications of cancer treatment or are provoked by systemic illness (metabolic derangements, infections). Evaluation should include a tailored history, neurologic examination, laboratory studies, neuroimaging, and electroencephalogram. In unprovoked seizures, antiepileptic drug (AED) treatment is required, and a nonenzyme-inducing AED is preferred. Treatment of the underlying cancer with surgery, chemotherapy, and radiation therapy also can help reduce seizures. Benzodiazepines are useful in the treatment of both provoked seizures and breakthrough epileptic seizures and as first-line treatment for status epilepticus. Counseling for safety is an important component in the care of a patient with cancer who has seizures. Good seizure management can be challenging but significantly improves the quality of life during all phases of care, including end-of-life care.