Histologically Confirmed Hippocampal Structural Features Revealed by 3T MR Imaging: Potential to Increase Diagnostic Specificity of Mesial Temporal Sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: With appropriate selection, temporal lobe epilepsy is potentially curable with surgical intervention achieving seizure freedom in ~80% of individuals. MR imaging-based identification of MTS remains central to the selection process but currently relies on qualitative visual analysis. We sought to determine if new ultrastructural hippocampal details seen on 3T MR imaging had histopathologic correlates and whether these could serve as a useful tool in MTS identification. MATERIALS AND METHODS: Patients undergoing selective anterior temporal lobectomy (n = 5) were scanned using 3T MR imaging preoperatively. En bloc resections were rescanned and subsequently prepared for histopathologic analysis of all hippocampal layers in the CA1-3 regions. Using a newly identified landmark from 3T FSTIR coronal images in 20 patients with histologically confirmed MTS, blinded studies compared ipsilateral and contralateral sides to generate threshold measurements for application in a fast quantitative analysis tool. RESULTS: Histopathologic analysis and correlation with 3T imaging of en bloc resections identified the low-intensity signal as the stratum lacunosum. MTS was associated with extensive gliosis throughout the CA1-3 regions, with loss of tissue thickness in the stratum pyramidale most pronounced in CA1. Fast quantitative analysis by using the stratum lacunosum as a landmark provided a test that identifies MTS with a SN of 70% and SP of 85%. CONCLUSIONS: Here we delineated ultrastructural hippocampal details seen on 3T MR imaging in both the in vivo and ex vivo setting, correlating these with histopathologic features consistent with MTS, and provided preliminary data suggesting their utility in the development of a quantitative analysis assessment tool for application in surgical-candidate selection.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".