Novel assessment of global metabolism by 18F-FDG-PET for localizing affected lobe in temporal lobe epilepsy

Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged ≥40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (χ² = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.

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.

Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

PURPOSE

18F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of 18F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients.

PATIENTS AND METHODS

We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis.

RESULT

Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms.

CONCLUSIONS

Visual analysis of 18F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing 18F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions.

Magnetic resonance imaging-based partial volume-corrected 18F-sodium fluoride positron emission tomography in the femoral neck

OBJECTIVES

18F-sodium fluoride (NaF) is a radiotracer used in PET that reflects calcium metabolism and osteoblastic activity. In this study, we assessed the construct validity of a novel application of global assessment to measure NaF uptake in the femoral neck as a method of evaluating physiologic changes in osteoblastic metabolism with age.

METHODS

Whole-body NaF-PET/computed tomography (CT) images and MRI of 24 male patients with a history of nonmetastatic prostate cancer between the ages of 36 and 82 years (67.8 ± 9.6) were analyzed. A region of interest delineated the entire femoral neck on the PET/CT image to determine the mean standardized uptake value (SUVmean). Correction for the partial volume effect was performed by measuring the volume of inert yellow bone marrow by MRI segmentation. Multiple linear regression was used to assess the relationship of uptake with age and body weight.

RESULTS

The SUVmean with and without partial volume correction decreased with respect to age (P = 0.001 and P = 0.002, respectively). Body weight was not significantly related to any measured PET parameter.

CONCLUSION

Our results support the use of global NaF uptake with magnetic resonance-derived partial volume correction in the femoral neck. Because osteoblastic metabolism is known to decrease with normal aging, the observed decrease in NaF uptake constitutes evidence for convergent validity, indicating that the proposed methodology likely reflects systemic osteoblastic activity. Future studies of this methodology are warranted in other instances of varying osteoblastic activity such as in metabolic bone diseases and for the evaluation of therapy targeting osteoblastic metabolism.

Global brain glucose uptake on 18F-FDG-PET/CT is influenced by chronic cardiovascular risk

PURPOSE

The goal of this study was to assess global cerebral glucose uptake in subjects with known cardiovascular risk factors by employing a quantitative 18F-fluorodeoxyglucose-PET/computed tomography (FDG-PET/CT) technique. We hypothesized that at-risk subjects would demonstrate decreased global brain glucose uptake compared to healthy controls.

METHODS

We compared 35 healthy male controls and 14 male subjects at increased risk for cardiovascular disease (CVD) as assessed by the systematic coronary risk evaluation (SCORE) tool. All subjects were grouped into two age-matched cohorts: younger (<50 years) and older (≥50 years). The global standardized uptake value mean (Avg SUVmean) was measured by mapping regions of interest of the entire brain across the supratentorial structures and cerebellum. Wilcoxon's rank-sum test was used to assess the differences in Avg SUVmean between controls and at-risk subjects.

RESULTS

Younger subjects demonstrated higher brain Avg SUVmean than older subjects. In addition, in both age strata, the 10-year risk for fatal CVD according to the SCORE tool was significantly greater in the at-risk groups than in healthy controls (younger: P = 0.0304; older: P = 0.0436). In the younger cohort, at-risk subjects demonstrated significantly lower brain Avg SUVmean than healthy controls (P = 0.0355). In the older cohort, at-risk subjects similarly had lower Avg SUVmean than controls (P = 0.0343).

CONCLUSIONS

Global brain glucose uptake appears to be influenced by chronic cardiovascular risk factors. Therefore, FDG-PET/CT may play a role in determining the importance of CVD on brain function and has potential for monitoring the efficacy of various therapeutic interventions.

Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

Background/aim

In temporal lobe epilepsy (TLE), brain positron emission tomography (PET) performed with F-18 fluorodeoxyglucose (FDG) is commonly used for lateralization of the epileptogenic temporal lobe. In this study, we aimed to evaluate the success of quantitative analysis of brain FDG PET images using data mining methods in the lateralization of the epileptogenic temporal lobe.

Materials and methods

Presurgical interictal brain FDG PET images of 49 adult mesial TLE patients with a minimum of 2 years of postsurgical follow-up and Engel I outcomes were retrospectively analyzed. Asymmetry indices were calculated from PET images from the mesial temporal lobe and its contiguous structures. The J48 and the logistic model tree (LMT) data mining algorithms were used to find classification rules for the lateralization of the epileptogenic temporal lobe. The classification results obtained by these rules were compared with the physicians’ visual readings and the findings of single-patient statistical parametric mapping (SPM) analyses in a test set of 18 patients. An additional 5-fold cross-validation was applied to the data to overcome the limitation of a relatively small sample size.

Results

In the lateralization of 18 patients in the test set, J48 and LMT methods were successful in 16 (89%) and 17 (94%) patients, respectively. The visual consensus readings were correct in all patients and SPM results were correct in 16 patients. The 5-fold cross- validation method resulted in a mean correct lateralization ratio of 96% (47/49) for the LMT algorithm. This ratio was 88% (43 / 49) for the J48 algorithm.

Conclusion

Lateralization of the epileptogenic temporal lobe with data mining methods using regional metabolic asymmetry values obtained from interictal brain FDG PET images in mesial TLE patients is highly accurate. The application of data mining can contribute to the reader in the process of visual evaluation of FDG PET images of the brain.

Potential Applications of PET-Based Novel Quantitative Techniques in Pediatric Diseases and Disorders

The progress made in hybrid PET imaging during the past decades has significantly expanded the role of this modality in both clinical and research applications. Semi-quantitative PET/CT has been the workhorse of clinical PET/CT due to its simplicity and availability. In addition to semi-quantitative PET/CT, volumetric PET and global metabolic activity have recently shown promise in a more accurate assessment of various diseases. PET/CT has been widely used in pediatric oncologic and non-oncologic diseases. Here we have highlighted few of the pitfalls in the quantitative PET/CT and their potential remedies which have potential in PET/CT evaluation of pediatric diseases.

The relationship between structural MRI, FDG-PET, and memory in temporal lobe epilepsy: Preliminary results

Structural and metabolic abnormalities of the temporal lobe are frequently found in temporal lobe epilepsy (TLE). In the present retrospective study, we investigated whether structural abnormalities evident in magnetic resonance imaging (MRI) and hypometabolism evident in [¹⁸F]fluorodeoxyglucose positron emission tomography (FDG-PET) independently influence verbal and nonverbal learning and delayed memory in patients with TLE. Sixty-eight patients with refractory unilateral TLE (35 left TLE, 33 right TLE) were divided into three groups: (1) no evidence of pathology in either MRI or FDG-PET studies (MRI-/PET-, n=15), (2) temporal FDG-PET determined hypometabolism with normal MRI findings (MRI-/PET+, n=21), and (3) evidence of temporal abnormalities in both MRI and FDG-PET studies (MRI+/PET+, n=32). A fourth group (MRI+/PET-, n=4) was too small for further statistical analysis and could not be included. Patients with MRI+/PET+ showed worse verbal memory than patients with MRI-/PET- (p<0.01), regardless of side of seizure focus. Verbal memory performance of patients with MRI-/PET+ was located between patients with MRI+/PET+ and MRI-/PET-, although group differences did not achieve statistical significance (ps>0.1). No group differences were found for nonverbal memory (p=0.27). Our results may suggest an interactive negative effect of metabolic and structural temporal lobe abnormalities on verbal memory. Still, our results are preliminary and need further validation by studies involving larger patient groups and up-to date quantitative imaging analysis methods.

Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.