Cost-Effectiveness of Advanced Imaging Technologies in the Presurgical Workup of Epilepsy

Integrating standard epilepsy protocol, ASL-perfusion, MP2RAGE/EDGE and the MELD-FCD classifier in the detection of subtle epileptogenic lesions: a 3 Tesla MRI pilot study

BACKGROUND

Malformations of cortical development (MCDs) in children with focal epilepsy pose significant diagnostic challenges, and a precise radiological diagnosis is crucial for surgical planning. New MRI sequences and the use of artificial intelligence (AI) algorithms are considered very promising in this regard, yet studies evaluating the relative contribution of each diagnostic technique are lacking.

METHODS

The study was conducted using a dedicated "EPI-MCD MR protocol" with a 3 Tesla MRI scanner in patients with focal epilepsy and previously negative MRI. MRI sequences evaluated included 3D FLAIR, 3D T1 MPRAGE, T2 Turbo Spin Echo (TSE), 3D T1 MP2RAGE, and Arterial Spin Labelling (ASL). Two paediatric neuroradiologists scored each sequence for localisation and extension of the lesion. The MELD-FCD AI classifier's performance in identifying pathological findings was also assessed. We only included patients where a diagnosis of MCD was subsequently confirmed on histology and/or sEEG.

RESULTS

The 3D FLAIR sequence showed the highest yield in detecting epileptogenic lesions, with 3D T1 MPRAGE, T2 TSE, and 3D T1 MP2RAGE sequences showing moderate to low yield. ASL was the least useful. The MELD-FCD classifier achieved a 69.2% true positive rate. In one case, MELD identified a subtle area of cortical dysplasia overlooked by the neuroradiologists, changing the management of the patient.

CONCLUSIONS

The 3D FLAIR sequence is the most effective in the MRI-based diagnosis of subtle epileptogenic lesions, outperforming other sequences in localisation and extension. This pilot study emphasizes the importance of careful assessment of the value of additional sequences.

Application of preoperative MRI lesion identification algorithm in pediatric and young adult focal cortical dysplasia-related epilepsy

OBJECTIVE

The purpose of this study was to evaluate the performance and generalizability of an automated, interpretable surface-based MRI classifier for the detection of focal cortical dysplasia.

METHODS

This was a retrospective cohort incorporating MRIs from the epilepsy surgery (FCD and MRI-negative) and neuroimaging (healthy controls) databases at Children's National Hospital (CNH), and a publicly-available FCD Type II dataset from Bonn, Germany. Clinical characteristics and outcomes were abstracted from patient records and/or existing databases. Subjects were included if they had 3T epilepsy-protocol MRI. Manually-segmented FCD masks were compared to the automated masks generated by the Multi-centre Epilepsy Lesion Detection (MELD) FCD detection algorithm. Sensitivity/specificity were calculated.

RESULTS

From CNH, 39 FCD pharmacoresistant epilepsy (PRE) patients, 19 healthy controls, and 19 MRI-negative patients were included. From Bonn, 85 FCD Type II were included, of which 68 passed preprocessing. MELD had varying performance (sensitivity) in these datasets: CNH FCD-PRE (54 %); Bonn (68 %); MRI-negative (44 %). In multivariate regression, FCD Type IIB pathology predicted higher chance of MELD automated lesion detection. All four patients who underwent resection/ablation of MELD-identified clusters achieved Engel I outcome.

SIGNIFICANCE

We validate the performance of MELD automated, interpretable FCD classifier in a diverse pediatric cohort with FCD-PRE. We also demonstrate the classifier has relatively good performance in an independent FCD Type II cohort with pediatric-onset epilepsy, as well as simulated real-world value in a pediatric population with MRI-negative PRE.

Determinants of successful ictal SPECT injection in phase 1 epilepsy presurgical evaluation: Findings from the pediatric epilepsy research consortium surgery database project

OBJECTIVES

The main goal of presurgical evaluation in drug-resistant focal epilepsy is to identify a seizure onset zone (SOZ). Of the noninvasive, yet resource-intensive tests available, ictal single-photon emission computed tomography (SPECT) aids SOZ localization by measuring focal increases in blood flow within the SOZ via intravenous peri-ictal radionuclide administration. Recent studies indicate that geographic and center-specific factors impact utilization of these diagnostic procedures. Our study analyzed successful ictal SPECT acquisition (defined as peri-ictal injection during inpatient admission) using surgery-related data from the Pediatric Epilepsy Research Consortium (PERC) surgery database. We hypothesized that a high seizure burden, longer duration of video EEG monitoring (VEEG), and more center-specific hours of SPECT availability would increase the likelihood of successful ictal SPECT.

METHODS

We identified study participants (≤18 years of age) who underwent SPECT as part of their phase 1 VEEG from January 2018 to June 2022. We assessed association between ictal SPECT outcomes (success vs. failure) and variables including patient demographics, epilepsy history, and center-specific SPECT practices.

RESULTS

Phase 1 VEEG monitoring with ictal SPECT injection was planned in 297 participants and successful in 255 participants (85.86%). On multivariable analysis, the likelihood of a successful SPECT injection was higher in patients of non-Hispanic ethnicity (p = 0.040), shorter duration VEEG (p = 0.004), and higher hours of available SPECT services (p < 0.001). Higher seizure frequency (p = 0.033) was significant only in bivariate analysis. Patients treated at centers with more operational hours were more likely to experience pre-admission protocols prior to VEEG (p = 0.002).

SIGNIFICANCE

There is inter-center variability in protocols and SPECT acquisition capabilities. Shorter duration of EEG monitoring, non-Hispanic ethnicity (when on private insurance), extended operational hours of nuclear medicine as noted on multivariate analysis and higher seizure frequency in bivariate analysis are strongly associated with successful ictal SPECT injection.

PLAIN LANGUAGE SUMMARY

In pediatric patients with drug-resistant epilepsy, single-photon emission computed tomography (SPECT) scans can be helpful in localizing seizure onset zone. However, due to many logistical challenges described below, which include not only the half-life of the technetium isotope used to inject intravenously during a seizure (called the ictal SPECT scan) but also available nuclear scanner time in addition to the unpredictability of seizures, obtaining an ictal SPECT during a planned elective inpatient hospital stay is not guaranteed. Thus, as healthcare costs increase, planning a prolonged hospital stay during which an ictal SPECT scan is not feasible is not optimal. We leveraged our prospective surgery database to look at center-specific factors and patient-specific factors associated with an ictal SPECT injection in the first, pediatric-focussed, large-scale, multicenter, prospective, SPECT feasibility study. We found that longer availability of the scanner is the most important center-specific factor in assuring ictal SPECT injection. Although seizure frequency is an important patient-specific factor on bivariate analysis, this factor lost statistical significance when other factors like patient insurance status and video EEG duration were also considered in our multivariable logistical model.

The added value of risk assessment and subsequent targeted treatment for epileptic seizures after stroke: An early-HTA analysis

INTRODUCTION

The development of post-stroke epilepsy (PSE) is related to a worse clinical outcome in stroke patients. Adding a biomarker to the clinical diagnostic process for the prediction of PSE may help to establish targeted and personalized treatment for high-risk patients, which could lead to improved patient outcomes. We assessed the added value of a risk assessment and subsequent targeted treatment by conducting an early Health Technology Assessment.

METHODS

Interviews were conducted with four relevant stakeholders in the field of PSE to obtain a realistic view of the current healthcare and their opinions on the potential value of a PSE risk assessment and subsequent targeted treatment. The consequences on quality of life and costs of current care of a hypothetical care pathway with perfect risk assessment were modeled based on information from a literature review and the input from the stakeholders. Subsequently, the maximum added value (the headroom) was calculated. Sensitivity analyses were performed to test the robustness of this result to variation in assumed input parameters, i.e. the accuracy of the risk assessment, the efficacy of anti-seizure medication (ASM), and the probability of patients expected to develop PSE.

RESULTS

All stakeholders considered the addition of a predictive biomarker for the risk assessment of PSE to be of value. The headroom amounted to €12,983. The sensitivity analyses demonstrated that the headroom remained beneficial when varying the accuracy of the risk assessment, the ASM efficacy, and the number of patients expected to develop PSE.

DISCUSSION

We showed that a risk assessment for PSE development is potentially valuable. This work demonstrates that it is worthwhile to undertake clinical studies to evaluate biomarkers for the prediction of patients at high risk for PSE and to assess the value of targeted prophylactic treatment.

Cost-effectiveness of HARNESS-MRI protocol in focal drug-resistant epilepsy in a limited-resources country: An Egyptian study

OBJECTIVES

The international league against epilepsy (ILAE) recommended the harmonized neuroimaging of epilepsy structural sequences (HARNESS-MRI) to improve the detection of epileptogenic lesions in patients with focal drug-resistant epilepsy (DRE). The application of this protocol is still limited in low-resource countries, mainly due to apparent high costs. We aimed to evaluate the cost-effectiveness of the HARNESS-MRI protocol in Egypt and highlighted our experience.

METHODS

Patients diagnosed with focal DRE at Cairo University epilepsy clinic underwent both conventional MRI (c-MRI) and HARNESS-MRI. Electro-clinical data were collected and analyzed. After the radiologists' initial diagnosis, a multidisciplinary team re-evaluated the MRI. Lesion detection rate and cost for detecting an extra lesion by HARNESS-MRI protocol were calculated.

RESULTS

The study included 230 patients with focal DRE (146, 62% males and 91, 38% females), with a mean age of 20.5 years. Epileptogenic lesions detected by c-MRI and HARNESS-MRI before and after the board meeting were 40, 106, and 131 lesions, respectively (P < 0.001). Sixty-nine percent of the lesions detected by HARNESS-MRI were missed on c-MRI; most commonly were mesial temporal sclerosis (MTS) and Malformations of cortical development (MCDs). Thirty-seven MTS and 32 MCDs were detected with HARNESS-MRI, compared to only 6 and 3, respectively, detected on c-MRI (P < 0.001). HARNESS-MR protocol is more cost-effective than c-MRI in detecting MRI lesions; it can save about 42$ for detecting an extra lesion in MRI.

CONCLUSION

The HARNESS-MRI protocol was cost-effective and highly recommended even in limited-resource countries for patients with focal DRE.

Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study

BACKGROUND AND OBJECTIVES

A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input.

METHODS

Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients.

RESULTS

Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification.

DISCUSSION

These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.

Epilepsy center characteristics and geographic region influence presurgical testing in the United States

OBJECTIVE

Persons with drug-resistant epilepsy may benefit from epilepsy surgery and should undergo presurgical testing to determine potential candidacy and appropriate intervention. Institutional expertise can influence use and availability of evaluations and epilepsy surgery candidacy. This census survey study aims to examine the influence of geographic region and other center characteristics on presurgical testing for medically intractable epilepsy.

METHODS

We analyzed annual report and supplemental survey data reported in 2020 from 206 adult epilepsy center directors and 136 pediatric epilepsy center directors in the United States. Test utilization data were compiled with annual center volumes, available resources, and US Census regional data. We used Wilcoxon rank-sum, Kruskal-Wallis, and chi-squared tests for univariate analysis of procedure utilization. Multivariable modeling was also performed to assign odds ratios (ORs) of significant variables.

RESULTS

The response rate was 100% with individual element missingness < 11% across 342 observations undergoing univariate analysis. A total of 278 complete observations were included in the multivariable models, and significant regional differences were present. For instance, compared to centers in the South, those in the Midwest used neuropsychological testing (OR = 2.87, 95% confidence interval [CI] = 1.2-6.86; p = .018) and fluorodeoxyglucose-positron emission tomography (OR = 2.74, 95% CI = = 1.14-6.61; p = .025) more commonly. For centers in the Northeast (OR = .46, 95% CI = .23-.93; p = .031) and West (OR = .41, 95% CI = .19-.87; p = .022), odds of performing single-photon emission computerized tomography were lower by nearly 50% compared to those in the South. Center accreditation level, demographics, volume, and resources were also associated with varying individual testing rates.

SIGNIFICANCE

Presurgical testing for drug-resistant epilepsy is influenced by US geographic region and other center characteristics. These findings have potential implications for comparing outcomes between US epilepsy centers and may inject disparities in access to surgical treatment.

Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection

Background and objective

The purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection.

Methods

The simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring.

Results

As a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed.

Conclusions

This study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.

HomotopicLI: Rationale, characteristics, and implications of a new threshold-free lateralization index of functional magnetic resonance imaging

The reliable preoperative estimation of brain hemispheric asymmetry may be achieved through multiple lateralization indices using functional magnetic resonance imaging. Adding to our previously developed AveLI, we devised a novel threshold-free lateralization index, HomotopicLI, which computes a basic formula, (Left - Right) / (Left + Right), using voxel values of pairs located symmetrically in relation to the midsagittal line as the terms Left and Right, and averages them within the regions-of-interest. The study aimed to evaluate HomotopicLI before clinical applications. Data were collected from 56 healthy participants who performed four language tasks. We compared seven index types, including HomotopicLI, AveLI, and BaseLI; BaseLI was calculated using the sums of voxel values as the terms. Contrary to our expectations, HomotopicLI performed similarly to AveLI but better than BaseLI in detecting right dominance. A detailed analysis of unilaterally activated voxels of the homotopic pairs revealed that unilateral activation occurred more frequently on the right than on the left when HomotopicLI indicated right dominance. The voxel values during right unilateral activation were smaller than those in the left, causing right dominances in the homotopic pairs by HomotopicLI. These unique features provide an advantage in detecting residual, compensative functions spreading weakly in the non-dominant hemisphere.

Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy

Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.

The Role of Magnetoencephalography and Single-Photon Emission Computed Tomography in Evaluation of Children With Drug-Resistant Epilepsy

Surgery holds the best outcomes for drug-resistant epilepsy in children, making localization of a seizure focus essential. However, there is limited research on the contribution of magnetoencephalography and single-photon emission computed tomography (SPECT) to the presurgical evaluation of lesional and nonlesional pediatric patients. This study proposed to evaluate the concordance of SPECT and magnetoencephalography (MEG) to scalp electroencephalography (EEG) to determine their effective contribution to the presurgical evaluation. On review, MEG and SPECT studies for 28 drug-resistant epilepsy cases were completed at Children's Hospital of Pittsburgh from May 2012 to August 2018. Although not reaching statistical significance, MEG had increased lobar concordance with EEG compared with SPECT (68% vs 46%). MEG or SPECT results effectively provided localization data leading to 6 surgical evaluations and 3 resections with outcomes of Engel class I or II at 12 months. This study suggests MEG and SPECT provide valuable localizing information for presurgical epilepsy evaluation of children with drug-resistant epilepsy.

Perspectives on the current developments with neuromodulation for the treatment of epilepsy

Introduction: As deep brain stimulation revolutionized the treatment of movement disorders in the late 80s, neuromodulation in the treatment of epilepsy will undoubtedly undergo transformative changes in the years to come with the exponential growth of technological development moving into mainstream practice; the appearance of companies such as Facebook, Google, Neuralink within the realm of brain-computer interfaces points to this trend.Areas covered: This perspective piece will talk about the history of brain stimulation in epilepsy, current-approved treatments, technical developments and the future of neurostimulation.Expert opinion: Further understanding of the brain alongside machine learning and innovative technology will be the future of neuromodulation for the treatment of epilepsy. All of these innovations and advances should pave the way toward overcoming the vexing underutilization of surgery in the therapeutic armamentarium against medically refractory seizures, given the implicit advantage of a neuromodulatory rather than neurodestructive approach.