Localizing Epileptic Foci Before Surgery in Patients With MRI-Negative Refractory Epilepsy Using Statistical Parameter Mapping and Three-Dimensional Stereotactic Surface Projection Based on 18F-FDG PET

Predictive model for epileptogenic tubers from all tubers in patients with tuberous sclerosis complex based on 18F-FDG PET: an 8-year single-centre study

BACKGROUND

More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images.

METHODS

Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis.

RESULTS

PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70-0.83) than the LDA (AUC = 0.7506; 95% CI 0.68-0.82) and ANN models (AUC = 0.7425; 95% CI 0.67-0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71-0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71-0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74-0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75-0.87).

CONCLUSIONS

The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.

Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy

OBJECTIVE

Normal interictal [18 F]FDG-PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co-registered to MRI) can then be used to compare epilepsy patients' predicted and clinical PET. We assessed the ability of SIPCOM to identify the Resection Zone (RZ) in patients with drug-resistant epilepsy (DRE) with reference to visual and statistical parametric mapping (SPM) analysis.

METHODS

Patients with complete presurgical work-up and subsequent SEEG and cortectomy were included. RZ localisation, the reference region, was assigned to one of eighteen anatomical brain regions. SIPCOM was implemented using healthy controls to train a GAN. To compare, the clinical PET coregistered to MRI was visually assessed by two trained readers, and a standard SPM analysis was performed.

RESULTS

Twenty patients aged 17-50 (32 ± 7.8) years were included, 14 (70%) with temporal lobe epilepsy (TLE). Eight (40%) were MRI-negative. After surgery, 14 patients (70%) had a good outcome (Engel I-II). RZ localisation rate was 60% with SIPCOM vs 35% using SPM (P = 0.015) and vs 85% using visual analysis (P = 0.54). Results were similar for Engel I-II patients, the RZ localisation rate was 64% with SIPCOM vs 36% with SPM. With SIPCOM localisation was correct in 67% in MRI-positive vs 50% in MRI-negative patients, and 64% in TLE vs 43% in extra-TLE. The average number of false-positive clusters was 2.2 ± 1.3 using SIPCOM vs 2.3 ± 3.1 using SPM. All RZs localized with SPM were correctly localized with SIPCOM. In one case, PET and MRI were visually reported as negative, but both SIPCOM and SPM localized the RZ.

SIGNIFICANCE

SIPCOM performed better than the reference computer-assisted method (SPM) for RZ detection in a group of operated DRE patients. SIPCOM's impact on epilepsy management needs to be prospectively validated.

In vivo laser speckle contrast imaging of 4-aminopyridine- or pentylenetetrazole-induced seizures

Clinical and preclinical studies on epileptic seizures are closely linked to the study of neurovascular coupling. Obtaining reliable information about cerebral blood flow (CBF) in the area of epileptic activity through minimally invasive techniques is crucial for research in this field. In our studies, we used laser speckle contrast imaging (LSCI) to gather information about the local blood circulation in the area of epileptic activity. We used two models of epileptic seizures: one based on 4-aminopyridine (4-AP) and another based on pentylenetetrazole (PTZ). We verified the duration of an epileptic seizure using electrocorticography (ECoG). We applied the antiepileptic drug topiramate (TPM) to both models, but its effect was different in each case. However, in both models, TPM had an effect on neurovascular coupling in the area of epileptic activity, as shown by both LSCI and ECoG data. We demonstrated that TPM significantly reduced the amplitude of 4-AP-induced epileptic seizures (4-AP+TPM: 0.61 ± 0.13 mV vs 4-AP: 1.08 ± 0.19 mV; p < 0.05), and it also reduced gamma power in ECoG in PTZ-induced epileptic seizures (PTZ+TPM: 38.5% ± 11.9% of the peak value vs PTZ: 59.2% ± 3.0% of peak value; p < 0.05). We also captured the pattern of CBF changes during focal epileptic seizures induced by 4-AP. Our data confirm that the system of simultaneous cortical LSCI and registration of ECoG makes it possible to evaluate the effectiveness of pharmacological agents in various types of epileptic seizures in in vivo models and provides spatial and temporal information on the process of ictogenesis.

Nuclear imaging for localization and surgical outcome prediction in epilepsy: A review of latest discoveries and future perspectives

Background

Epilepsy is one of the most common neurological disorders. Approximately, one-third of patients with epilepsy have seizures refractory to antiepileptic drugs and further require surgical removal of the epileptogenic region. In the last decade, there have been many recent developments in radiopharmaceuticals, novel image analysis techniques, and new software for an epileptogenic zone (EZ) localization.

Objectives

Recently, we provided the latest discoveries, current challenges, and future perspectives in the field of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in epilepsy.

Methods

We searched for relevant articles published in MEDLINE and CENTRAL from July 2012 to July 2022. A systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis was conducted using the keywords "Epilepsy" and "PET or SPECT." We included both prospective and retrospective studies. Studies with preclinical subjects or not focusing on EZ localization or surgical outcome prediction using recently developed PET radiopharmaceuticals, novel image analysis techniques, and new software were excluded from the review. The remaining 162 articles were reviewed.

Results

We first present recent findings and developments in PET radiopharmaceuticals. Second, we present novel image analysis techniques and new software in the last decade for EZ localization. Finally, we summarize the overall findings and discuss future perspectives in the field of PET and SPECT in epilepsy.

Conclusion

Combining new radiopharmaceutical development, new indications, new techniques, and software improves EZ localization and provides a better understanding of epilepsy. These have proven not to only predict prognosis but also to improve the outcome of epilepsy surgery.