Diagnostic performance of neuroimaging modalities for epileptogenic focus localization: A systematic review
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Accurate localization of epileptogenic foci remains of significant importance for surgical planning in drug-resistant epilepsy. Multiple neuroimaging modalities are available; however, their comparative diagnostic performance lacks comparative detailed synthesis. This systematic review aimed to evaluate and compare the diagnostic accuracy of structural MRI, PET imaging, SPECT/SISCOM, and combined multimodal strategies for epileptogenic focus localization. METHODS: We conducted a systematic review following PRISMA 2020 guidelines, searching PubMed, Scopus, Google Scholar, Cochrane Library, and Web of Science databases up to May 30, 2025. Studies evaluating the diagnostic performance of neuroimaging modalities for epilepsy focus localization with surgical correlation were included. Data extraction focused on sensitivity, specificity, and clinical manner. Quality assessment used QUADAS-2 criteria. RESULTS: Fifteen studies included a total of 1157 patients that met inclusion criteria. Combined multimodal strategies integrating two or more imaging modalities demonstrated the highest diagnostic performance (sensitivity 82-100%), followed by structural MRI in lesional epilepsy (72-100% sensitivity). PET imaging showed consistent performance across clinical contexts (33-89% sensitivity), while SPECT/SISCOM exhibited variable results (33-83% sensitivity). Strong complementarity existed between MRI and PET (85% concordance), with context-dependent optimization for lesional versus non-lesional epilepsy. SIGNIFICANCE: Combined multimodal neuroimaging provides superior diagnostic performance for epileptogenic focus localization. Clinical context significantly impacts the modality selection, with MRI prioritized in lesional cases and functional imaging essential for MRI-negative epilepsy. These findings support evidence-based imaging protocols for surgical epilepsy evaluation. PLAIN LANGUAGE SUMMARY: This systematic review evaluated which brain imaging techniques are best for finding the exact location where seizures start in people with drug-resistant epilepsy who need surgery. The researchers analyzed 15 studies involving 1157 patients. They found that using multiple imaging techniques together (combining structural and functional imaging) provides the most accurate results, with success rates of 82-100%. Standard MRI scans work very well (72-100% accuracy) when there is a visible brain abnormality causing seizures. However, for patients whose MRI looks normal, additional functional imaging techniques like PET or SPECT scans are crucial, achieving 63-89% accuracy. The study shows that the best imaging approach depends on the individual patient's situation: MRI should be used first when a brain lesion is suspected, but functional imaging becomes essential when MRI does not show anything abnormal. These findings help doctors choose the right combination of imaging tests for each patient to improve surgical planning and outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it