The Value of Regional Cerebral Blood Flow SPECT and FDG PET in Operculoinsular Epilepsy
Bibliographic record
Abstract
PURPOSE: Operculoinsular epilepsy (OIE) can be challenging to diagnose. While the value of SPECT cerebral blood flow and PET F-FDG studies for presurgical evaluation of patients with medial temporal lobe epilepsy (MTLE) is well established, it remains unclear whether they can help identify an operculoinsular epileptic focus. This study assesses the value of interictal/ictal regional cerebral blood flow (rCBF) SPECT and FDG PET for OIE diagnosis. METHODS: Eighteen patients with proven OIE who underwent interictal/ictal rCBF SPECT and/or FDG-PET prior to epilepsy surgery were identified from our clinical database and were compared with a group of 18 patients who underwent MTLE surgery. Regional cerebral blood flow SPECT and FDG PET images were reevaluated visually by an expert reader blind to clinical data. RESULTS: Interictal/ictal rCBF SPECT correctly identified an operculoinsular focus in 11 (65%) of 17 OIE patients and was misleading in 3 cases (18%). Secondary activation in areas connected to the insula was often observed. In the MTLE group, the area of maximal increased perfusion was congruent in 12 (75%) of 16 patients and extended to the ipsilateral insula in 1 patient. FDG PET findings were concordant with the epileptic focus in 8 (47%) of 17 OIE patients and were misleading in 4 (24%), whereas they were concordant in all MTLE patients. CONCLUSIONS: Interictal/ictal rCBF SPECT can identify a concordant operculoinsular focus in a significant proportion of OIE patients and offers a valuable diagnostic tool in nonlesional cases. By contrast, the value of interictal FDG PET in this population is more equivocal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".