Dynamic 18F-FDG-PET kinetic parameters for epileptogenic zone localization in drug-resistant epilepsy
Bibliographic record
Abstract
Objective: Precisely localizing the seizure onset zone remains a challenging task in drug-resistant epilepsy (DRE) patients especially given its critical role in successful surgery and effective management. This study aimed to investigate the kinetic parameters of regional 18 F-fluorodeoxyglucose (FDG) uptake in DRE patients, aiming to identify the kinetic parameters best enabling the identification of the epileptogenic region. Methods: Consecutive DRE patients with clinically mandated interictal 18 F-FDG PET/CT were recruited from October 2019 to September 2020 for pre-surgical evaluation. Immediately after injecting 18 F-FDG of 112–179 MBq, dynamic data were acquired for 90 min. The motion correction and resampling to the Montreal atlas was performed in order to generate a transformation matrix. 116 volume of interests (VOIs) and regional time-activity curves (TACs) were generated by employing the automated anatomical labeling (AAL) template using PMOD software. Kinetic parameters of FDG unidirectional blood-brain clearance ( K 1 ), efflux ( k 2 ), phosphorylation ( k 3 ), and net metabolic flux ( K i ) were derived using irreversible 2-tissue-compartment model with an image-derived input function (IDIF). The kinetic parameters values obtained from all regions were ranked and compared with the presumed epileptogenic zone (EZ). Results: Eleven DRE patients (5 males, 6 females, mean age 35.1 ± 10.2 years) were analyzed. We found that the region with the lowest values of K i provided correct lateralization in 7/7 (100%) of patient with temporal lobe epilepsy (TLE) and the region with the lowest K i and k 3 parameters showed concordance with the EZ in 100% and 71.4% of patients, respectively. Conclusion: The present parametric approach to the evaluation of FDG-PET may be more sensitive than semi-quantitative approaches for the detection of pathophysiology in the EZ of patients with medically unresponsive TLE in addition to the routine clinical investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".