Detection Of Focal Cortical Dysplasia Research Articles

A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.

Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics. We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models. Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign (P < 0.001) and cortical thickening (P < 0.001). In contrast, AI models were sensitive to abnormal gyration (P < 0.01) or gray-white matter blurring (P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs (P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) (P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17). This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.

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

ObjectiveThe 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. MethodsThis 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. ResultsFrom 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. SignificanceWe 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.

MRI-quality and morphometric MRI analysis to identify focal cortical dysplasia: An exploratory study

BackgroundIn the pre-surgical evaluation of people with focal epilepsy and a normal MRI, Morphometric Analysis Program v2018 (MAP18) aids in detecting visually inconspicuous focal cortical dysplasia (FCD). We investigated the impact of MRI scans with reduced signal-to-noise ratio (SNR) and spatial resolution (SR) on FCD detection by MAP18, aiming to improve the chances of achieving seizure freedom through epilepsy surgery. MethodsThirty MRI scans with the identified lesion using MAP18 radiologically confirmed as FCD by a neuroradiologist, were retrospective analysed. SNR and SR were artificially reduced in ten steps, and their impact on MAP18 outcomes was assessed using multilevel analysis. ResultsThere was a significant effect after reducing SR and SNR for z-score and volume of the FCD cluster, the total number of detected clusters, and volume of these clusters. After SNR reduction, there was also a significant effect for z-score of the total number of detected clusters. FCD became undetectable by MAP18 after six steps of SR reduction (voxel size 2.8 × 2.8 × 2.8 mm³) and after two steps of SNR reduction. ConclusionsThis exploratory study suggests that reduced SR and SNR negatively affect FCD detection with MRI post-processing (MAP18). The MAP18 evaluator should screen MRI quality before post-processing, particularly for scans with significant visual noise or voxel sizes of 2.8 × 2.8 × 2.8 mm³ and upwards, as repeating a low-quality MRI scan is less burdensome than the adverse effects of continued seizures due to failure to detect FCD.

Combining magnetic resonance fingerprinting with voxel-based morphometric analysis to reduce false positives for focal cortical dysplasia detection.

We aim to improve focal cortical dysplasia (FCD) detection by combining high-resolution, three-dimensional (3D) magnetic resonance fingerprinting (MRF) with voxel-based morphometric magnetic resonance imaging (MRI) analysis. We included 37 patients with pharmacoresistant focal epilepsy and FCD (10 IIa, 15 IIb, 10 mild Malformation of Cortical Development [mMCD], and 2 mMCD with oligodendroglial hyperplasia and epilepsy [MOGHE]). Fifty-nine healthy controls (HCs) were also included. 3D lesion labels were manually created. Whole-brain MRF scans were obtained with 1 mm3 isotropic resolution, from which quantitative T1 and T2 maps were reconstructed. Voxel-based MRI postprocessing, implemented with the morphometric analysis program (MAP18), was performed for FCD detection using clinical T1w images, outputting clusters with voxel-wise lesion probabilities. Average MRF T1 and T2 were calculated in each cluster from MAP18 output for gray matter (GM) and white matter (WM) separately. Normalized MRF T1 and T2 were calculated by z-scores using HCs. Clusters that overlapped with the lesion labels were considered true positives (TPs); clusters with no overlap were considered false positives (FPs). Two-sample t-testswere performed to compare MRF measures between TP/FP clusters. A neural network model was trained using MRF values and cluster volume to distinguish TP/FP clusters. Ten-fold cross-validation was used to evaluate model performance at the cluster level. Leave-one-patient-out cross-validation was used to evaluate performance at the patient level. MRF metrics were significantly higher in TP than FP clusters, including GM T1, normalized WM T1, and normalized WM T2. The neural network model with normalized MRF measures and cluster volume as input achieved mean area under the curve (AUC) of .83, sensitivity of 82.1%, and specificity of 71.7%. This model showed superior performance over direct thresholding of MAP18 FCD probability map at both the cluster and patient levels, eliminating ≥75% FP clusters in 30% of patients and ≥50% of FP clusters in 91% of patients. This pilot study suggests the efficacy of MRF for reducing FPs in FCD detection, due to its quantitative values reflecting invivo pathological changes. © 2024 International League Against Epilepsy.

Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography

Purpose: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN). Methods: MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN. Results: On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65. Significance: Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection.

Enhanced focal cortical dysplasia detection in pediatric frontal lobe epilepsy with asymmetric radiomic and morphological features.

Focal cortical dysplasia (FCD) is the most common pathological cause for pediatric epilepsy, with frontal lobe epilepsy (FLE) being the most prevalent in the pediatric population. We attempted to utilize radiomic and morphological methods on MRI and PET to detect FCD in children with FLE. Thirty-seven children with FLE and 20 controls were included in the primary cohort, and a five-fold cross-validation was performed. In addition, we validated the performance in an independent site of 12 FLE children. A two-stage experiments including frontal lobe and subregions were employed to detect the lesion area of FCD, incorporating the asymmetric feature between the left and right hemispheres. Specifically, for the radiomics approach, we used gray matter (GM), white matter (WM), GM and WM, and the gray-white matter boundary regions of interest to extract features. Then, we employed a Multi-Layer Perceptron classifier to achieve FCD lesion localization based on both radiomic and morphological methods. The Multi-Layer Perceptron model based on the asymmetric feature exhibited excellent performance both in the frontal lobe and subregions. In the primary cohort and independent site, the radiomics analysis with GM and WM asymmetric features had the highest sensitivity (89.2 and 91.7%) and AUC (98.9 and 99.3%) in frontal lobe. While in the subregions, the GM asymmetric features had the highest sensitivity (85.6 and 79.7%). Furthermore, relying on the highest sensitivity of GM and WM asymmetric features in frontal lobe, when integrated with the subregions results, our approach exhibited overlaps with GM asymmetric features (55.4 and 52.4%), as well as morphological asymmetric features (54.4 and 53.8%), both in the primary cohort and at the independent site. This study demonstrates that a two-stage design based on the asymmetry of radiomic and morphological features can improve FCD detection. Specifically, incorporating regions of interest for GM, WM, GM, and WM, and the gray-white matter boundary significantly enhances the localization capabilities for lesion detection within the radiomics approach.

Edge-Enhancing Gradient Echo MRI at 7T for detection of focal cortical dysplasia in epilepsy

3D-Edge Enhancing Gradient Echo MRI (EDGE) is a promising method for identifying focal cortical dysplasia (FCD) because of its superior contrast-to-noise ratio. Gains in signal-to-noise ratio (SNR) and spatial resolution using 7T MRI should further enhance the utility of EDGE; however, the exquisite sensitivity to the optimal inversion time makes translation to 7T challenging given the extreme nonuniformities in the transmit field (B1+). By utilizing a combined MP2RAGE-EDGE sequence, the efficiency of the acquisition can be optimized to improve B1+ transmit uniformity and SNR. Using this approach, we report the first experience of using 7T EDGE MRI for the diagnosis of FCD in epilepsy, showing the feasibility of achieving high spatial resolution, SNR, and image contrast uniformity. EDGE-MP2RAGE sequence improves the detection of gray-white matter junction abnormalities associated with FCD and is a valuable, time-efficient addition to a 7T MR protocol in patients with focal epilepsy.

An open presurgery MRI dataset of people with epilepsy and focal cortical dysplasia type II

Automated detection of lesions using artificial intelligence creates new standards in medical imaging. For people with epilepsy, automated detection of focal cortical dysplasias (FCDs) is widely used because subtle FCDs often escape conventional neuroradiological diagnosis. Accurate recognition of FCDs, however, is of outstanding importance for affected people, as surgical resection of the dysplastic cortex is associated with a high chance of postsurgical seizure freedom. Here, we make publicly available a dataset of 85 people affected by epilepsy due to FCD type II and 85 healthy control persons. We publish 3D-T1 and 3D-FLAIR, manually labeled regions of interest, and carefully selected clinical features. The open presurgery MRI dataset may be used to validate existing automated algorithms of FCD detection as well as to create new approaches. Most importantly, it will enable comparability of already existing approaches and support a more widespread use of automated lesion detection tools.

Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice.

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%-50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.

Edge-Enhancing Gradient-Echo MP2RAGE for Clinical Epilepsy Imaging at 7T.

The 3D edge-enhancing gradient-echo (EDGE) MR imaging sequence offers superior contrast-to-noise ratio in the detection of focal cortical dysplasia. EDGE could benefit from 7T MR imaging but also faces challenges such as image inhomogeneity and low acquisition efficiency. We propose an EDGE-MP2RAGE sequence that can provide both EDGE and T1-weighted contrast, simultaneously, improving data-acquisition efficiency. We demonstrate that with sequence optimization, EDGE images with sufficient uniformity and T1-weighted images with high gray-to-white matter contrast can be achieved.

Preoperative Detection of Subtle Focal Cortical Dysplasia in Children by Combined Arterial Spin Labeling, Voxel-Based Morphometry, Electroencephalography-Synchronized Functional MRI, Resting-State Regional Homogeneity, and 18F-fluorodeoxyglucose Positron Emission Tomography.

Focal cortical dysplasia (FCD) causes drug-resistant epilepsy in children that can be cured surgically, but the lesions are often unseen by imaging. To assess the efficiency of arterial spin labeling (ASL), voxel-based-morphometry (VBM), fMRI electroencephalography (EEG), resting-state regional homogeneity (ReHo), 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), and their combination in detecting pediatric FCD. We prospectively included 10 children for whom FCD was localized by surgical resection. They underwent 3T MR acquisition with concurrent EEG, including ASL perfusion, resting-state BOLD fMRI (allowing the processing of EEG-fMRI and ReHo), 3D T1-weighted images processed using VBM, and FDG PET-CT coregistered with MRI. Detection was assessed visually and by comparison with healthy controls (for ASL and VBM). Eight children had normal MRI, and 2 had asymmetric sulci. Using MR techniques, FCD was accurately detected by ASL for 6/10, VBM for 5/10, EEG-fMRI for 5/8 (excluding 2 with uninterpretable results), and ReHo for 4/10 patients. The combination of ASL, VBM, and ReHo allowed correct FCD detection for 9/10 patients. FDG PET alone showed higher accuracy than the other techniques (7/9), and its combination with VBM allowed correct FCD detection for 8/9 patients. The detection efficiency was better for patients with asymmetric sulci (2/2 for all techniques), but advanced MR techniques and PET were useful for MR-negative patients (7/8). A combination of multiple imaging techniques, including PET, ASL, and VBM analysis of T1-weighted images, is effective in detecting subtle FCD in children.

Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.

Fully automated detection of focal cortical dysplasia: Comparison of MPRAGE and MP2RAGE sequences.

The detection of focal cortical dysplasia (FCD) in magnetic resonance imaging is challenging. Voxel-based morphometric analysis and automated FCD detection using an artificial neural network (ANN) integrated into the Morphometric Analysis Program (MAP18) have been shown to facilitate FCD detection. This study aimed to evaluate whether the detection of FCD can be further improved by feeding this approach with magnetization prepared two rapid acquisition gradient echoes (MP2RAGE) instead of magnetization-prepared rapid acquisition gradient echo (MPRAGE) datasets. MPRAGE and MP2RAGE datasets were acquired in a consecutive sample of 32 patients with FCD and postprocessed using MAP18. Visual analysis and, if available, histopathology served as the gold standard for assessing the sensitivity and specificity of FCD detection. Out-of-sample specificity was evaluated in a cohort of 32healthy controls. The sensitivity and specificity of FCD detection were 82.4% and 62.5% for the MPRAGE and 97.1% and 34.4% for the MP2RAGE sequences, respectively. Median volumes of true-positive voxel clusters were .16ml for the MPRAGE and .52ml for the MP2RAGE sequences compared to .08- and .04-ml volumes of false-positive clusters. With regard to cluster volumes, FCD detection was substantially improved for the MP2RAGE data when the estimated optimal threshold of .23ml was applied (sensitivity = 72.9%, specificity = 83.0%). In contrast, the estimated optimal threshold of .37ml for the MPRAGE data did not improve FCD lesion detection (sensitivity = 42.9%, specificity = 79.5%). In this study, the sensitivity of FCD detection by morphometric analysis and an ANN integrated into MAP18 was higher for MP2RAGE than for MPRAGE sequences. Additional usage of cluster volume information helped to discriminate between true- and false-positive MP2RAGE results.

Malformations of cortical development as models of altered brain excitability

The developmental organisation of the human cerebral cortex is among the most complex processes known in biology. The process includes the migration of neuronal precursors from the cortical endplate to the future outer boundary of the brain, a consistent organisation in layers from the inside to the outside, the differentiation of principal neurons and a wide spectrum of inhibitory neuronal types focusing information processing and controlling excitability, and the precision wiring of neurons within cortical columns and in extended projections. This process results in an efficient, stable, and functional small-world brain network. Unsurprisingly, the complex sequences and interactions contributing to this cortical development are liable to minor and major disturbances. Progress in brain imaging and in the histological analysis of brain specimens, particularly from humans undergoing neurosurgical resections of brain areas considered pathological, has considerably increased knowledge from the spectrum of malformations of cortical development. Such malformations can be extensive (eg, the formation of a double cortex or absent cortical layering, or altered gyrification, such as in pachygyria, or can be circumscribed (as with focal cortical dysplasia), or can be space occupying (as with a developmental tumour). Abnormal neuronal migration, differentiation, and wiring will often affect the intricate balance of excitability and inhibition in the affected brain region, resulting in epilepsy. Improved high resolution brain imaging, including computer-assisted postprocessing of 3D data sets, 1 Gill RS Lee HM Caldairou B et al. Multicenter validation of a deep learning detection algorithm for focal cortical dysplasia. Neurol. 2021; (published online Sep 14)https://doi.org/10.1212/WNL.0000000000012698 Crossref Scopus (10) Google Scholar , 2 Demerath T Rubensdörfer L Schwarzwald R et al. Morphometric MRI analysis: improved detection of focal cortical dysplasia using the MP2RAGE sequence. AJNR Am J Neuroradiol. 2020; 41: 1009-1014 Crossref PubMed Scopus (12) Google Scholar has provided evidence that discrete focal brain malformations are the most common cause of focal epilepsy in people, and particularly of focal epilepsy that responds insufficiently to anti-seizure medication and thus requires surgical treatment. 3 Fauser S Essang C Altenmüller DM et al. Long-term seizure outcome in 211 patients with focal cortical dysplasia. Epilepsia. 2015; 56: 66-76 Crossref PubMed Scopus (113) Google Scholar , 4 Blümcke I Spreafico R Haaker G et al. EEBB Consortium. Histopathological findings in brain tissue obtained during epilepsy surgery. N Engl J Med. 2017; 377: 1648-1656 Crossref PubMed Scopus (370) Google Scholar Neocortical development and epilepsy: insights from focal cortical dysplasia and brain tumoursDuring the past decade, there have been considerable advances in understanding of the genetic and morphogenic processes underlying cortical malformations and developmental brain tumours. Focal malformations are caused by somatic (postzygotic) variants in genes related to cell growth (ie, in the mTOR pathway in focal cortical dysplasia type 2), which are acquired in neuronal progenitors during neurodevelopment. In comparison, developmental brain tumours result from somatic variants in genes related to cell proliferation (eg, in the MAP-kinase pathway in ganglioglioma), which affect proliferating glioneuronal precursors. Full-Text PDF

\u201cWithin a minute\u201d detection of focal cortical dysplasia

PurposeTo evaluate a MRI postprocessing tool for the enhanced and rapid detection of focal cortical dysplasia (FCD).MethodsMP2RAGE sequences of 40 consecutive, so far MRI-negative patients and of 32 healthy controls were morphometrically analyzed to highlight typical FCD features. The resulting morphometric maps served as input for an artificial neural network generating a FCD probability map. The FCD probability map was inversely normalized, co-registered to the MPRAGE2 sequence, and re-transferred into the PACS system. Co-registered images were scrolled through “within a minute” to determine whether a FCD was present or not.ResultsFifteen FCD, three subcortical band heterotopias (SBH), and one periventricular nodular heterotopia were identified. Of those, four FCD and one SBH were only detected by MRI postprocessing while one FCD and one focal polymicrogryia were missed, respectively. False-positive results occurred in 21 patients and 22 healthy controls. However, true positive cluster volumes were significantly larger than volumes of false-positive clusters (p < 0.001). The area under the curve of the receiver operating curve was 0.851 with a cut-off volume of 0.05 ml best indicating a FCD.ConclusionAutomated MRI postprocessing and presentation of co-registered output maps in the PACS allowed for rapid (i.e., “within a minute”) identification of FCDs in our clinical setting. The presence of false-positive findings currently requires a careful comparison of postprocessing results with conventional MR images but may be reduced in the future using a neural network better adapted to MP2RAGE images.

Intraoperative Ultrasound Shear-Wave Elastography in Focal Cortical Dysplasia Surgery.

Previous studies reported interest in intraoperative shear-wave elastography (SWE) guidance for brain-tumor and epilepsy surgeries. Focal cortical dysplasia (FCD) surgery is one of the most appropriate indications for using SWE guidance. The aim of this study was to evaluate the efficacy of ultrasound SWE techniques for the intraoperative detection of FCDs. We retrospectively analyzed data from 18 adult patients with drug-resistant epilepsy associated with FCD who had undergone SWE-guided surgery. Conventional B-mode images detected FCD in 2 patients (11.1%), while SWE detected FCD in 14 patients (77.8%). The stiffness ratios between MRI-positive and -negative cases were significantly different (3.6 ± 0.4 vs. 2.2 ± 0.6, respectively; p < 0.001). FCDs were significantly more frequently detected by interoperative SWE in women (OR 4.7, 95% CI (1.7–12.7); p = 0.004) and in patients in whom FCD was visible on magnetic resonance imaging (MRI; OR 2.3, 95% CI (1.3–4.3); p = 0.04). At 1 year after surgery and at last follow-up (mean = 21 months), seizure outcome was good (International League Against Epilepsy (ILAE) Class 1 or 2) in 72.2% and 55.6% of patients, respectively. Despite some limitations, our study highlighted the potential of SWE as an intraoperative tool to detect FCD. Future technical developments should allow for optimizing intraoperative surgical-cavity evaluation from the perspective of complete FCD resection. Interobserver reliability of SWE measurements should also be assessed by further studies.

External validation of automated focal cortical dysplasia detection using morphometric analysis.

Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magnetic resonance imaging (MRI) assessment. The visual detection can be facilitated by morphometric analysis of T1-weighted images, for example, using the Morphometric Analysis Program (v2018; MAP18), which was introduced in 2005, independently validated for its clinical benefits, and successfully integrated in standard presurgical workflows of numerous epilepsy centers worldwide. Here we aimed to develop an artificial neural network (ANN) classifier for robust automated detection of FCDs based on these morphometric maps and probe its generalization performance in a large, independent data set. In this retrospective study, we created a feed-forward ANN for FCD detection based on the morphometric output maps of MAP18. The ANN was trained and cross-validated on 113 patients (62 female, meanage ± SD=29.5±13.6years) with manually segmented FCDs and 362 healthy controls (161 female, mean age ± SD=30.2±9.6years) acquired on 13 different scanners. In addition, we validated the performance of the trained ANN on an independent, unseen data set of 60 FCD patients (28 female, mean age ± SD=30±15.26years) and 70 healthy controls (42 females, mean age ± SD = 40.0±12.54years). In the cross-validation, the ANN achieved a sensitivity of 87.4% at a specificity of 85.4% on the training data set. On the independent validation data set, our method still reached a sensitivity of 81.0% at a comparably high specificity of 84.3%. Our method shows a robust automated detection of FCDs and performance generalizability, largely independent of scanning site or MR-sequence parameters. Taken together with the minimal input requirements of a standard T1 image, our approach constitutes a clinically viable and useful tool in the presurgical diagnostic routine for drug-resistant focal epilepsy.

Improving surgical outcome with electric source imaging and high field magnetic resonance imaging

While most patients with focal epilepsy present with clear structural abnormalities on standard, 1.5 or 3 T MRI, some patients are MRI-negative. For those, quantitative MRI techniques, such as volumetry, voxel-based morphometry, and relaxation time measurements can aid in finding the epileptogenic focus. High-field MRI, just recently approved for clinical use by the FDA, increases the resolution and, in several publications, was shown to improve the detection of focal cortical dysplasias and mild cortical malformations. For those cases without any tissue abnormality in neuroimaging, even at 7 T, scalp EEG alone is insufficient to delimitate the epileptogenic zone. They may benefit from the use of high-density EEG, in which the increased number of electrodes helps improve spatial sampling. The spatial resolution of even low-density EEG can benefit from electric source imaging techniques, which map the source of the recorded abnormal activity, such as interictal epileptiform discharges, focal slowing, and ictal rhythm. These EEG techniques help localize the irritative, functional deficit, and seizure-onset zone, to better estimate the epileptogenic zone. Combining those technologies allows several drug-resistant cases to be submitted to surgery, increasing the odds of seizure freedom and providing a must needed hope for patients with epilepsy.

Improved detection of focal cortical dysplasia in normal-appearing FLAIR images using a Bayesian classifier.

Focal cortical dysplasia (FCD) is a malformation of cortical development that often causes pharmacologically intractable epilepsy. However, FCD lesions are frequently characterized by minor structural abnormalities that can easily go unrecognized, making diagnosis difficult. Therefore, many epileptic patients have had pathologically confirmed FCD lesions that appeared normal in pre-surgical fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) studies. Such lesions are called "FLAIR-negative." This study aimed to improve the detection of histopathologically verified FCD in a sample of patients without visually appreciable lesions. The technique first extracts a series of features from a FLAIR image. Then, three naive Bayesian classifiers with probability (NBCP) are trained based on different numbers of feature maps to classify voxels as lesional or healthy voxels and assign the lesions a probability of correct classification. This method classifies the three-dimensional (3D) images of all patients using leave-one-out cross-validation (LOOCV). Finally, the 3D lesion probability map, including epileptogenic lesions, is obtained by removing false-positive voxel outliers using the morphological method. The performance of the NBCP was assessed for quantitative analysis by specificity, accuracy, recall, precision, and Dice coefficient in subject-wise, lesion-wise, and voxel-wise manners. The best detection results were obtained by using four features: cortical thickness, symmetry, K-means, and modified texture energy. There were eight lesions in seven patients. The subject-wise sensitivity of the proposed method was 85.71% (6/7). Seven out of eight lesions were detected, so the lesion-wise sensitivity was 87.50% (7/8). No significant differences in effectiveness were found between automated lesion detection using four features and lesion detection using manual segmentation, as voxels were quantitatively analyzed in terms of specificity (mean±SD=99.64±0.13), accuracy (mean±SD=99.62±0.14), recall (mean±SD=73.27±26.11), precision (mean±SD=11.93±8.16), and Dice coefficient (mean±SD=22.82±15.57). We developed a novel automatic voxel-based method to improve the detection of FCD FLAIR-negative lesions. To the best of our knowledge, this study is the first to detect FCD lesions that appear normal in pre-surgical 3D high-resolution FLAIR images alone with a limited number of radiomics features. We optimized the algorithm and selected the best prior probability to improve the detection. For non-temporal lobe epilepsy (non-TLE) patients, lesions could be accurately located, although there were still false-positive areas.

Brain MRI post-processing with MAP07 in the preoperative evaluation of patients with pharmacoresistant epilepsy – Croatian single centre experience

ObjectiveThis study aimed to determine the role of brain MRI post-processing method MAP07 (Morphometric Analysis Program) in detecting epileptogenic brain lesions in patients with pharmacoresistant epilepsy (PE). MAP07 is a sophisticated diagnostic program that offers several morphometric maps and facilitates the detection and localization of hippocampal sclerosis (HS), focal cortical dysplasias (FCD), and other types of cortical malformations, which could be undetected by conventional visual MRI analysis (CVA). Methods120 patients aged > 16 years with PE have been recruited. 3 T MRI was performed according to epilepsy imaging protocol followed by image postprocessing with a fully automated MATLAB script, MAP07, by applying SPM5 algorithms. Statistical analysis was performed in IBM SPSS Statistics, version 25.0. ResultsAnalysis in our patients showed a high sensitivity of MAP07 with low specificity and with a high proportion of false-positive patients. After MRI analysis, out of 120 patients, 32 were found to have no structural abnormalities by conventional visual analysis in whom after MAP07 in 5 patients structural lesions were found (in one HS, in one FCD, in two perinatal vascular lesions, and in one hippocampal hyperintensity). There was a quite high overall coincidence of the findings of MAP07 and MRI for the detection of FCD, HS, perinatal ischemia/chronic vascular lesions, heterotopias, and polymicrogyria (kappa coefficient above 0.700). ConclusionsMAP07 analysis is a useful, additional, and automated method that may guide re-evaluation of MRI by highlighting suspicious cortical regions, as a complementary method to CVA, by enhancing the visualization of cortical malformations and lesions.