Advanced tractography-guided laser ablation of a perirolandic long-term epilepsy–associated tumor: illustrative case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Microsurgical resection of drug-resistant epilepsy-associated perirolandic lesions can lead to postoperative motor impairment. Magnetic resonance imaging (MRI)-guided laser interstitial thermal therapy (MRgLITT) has emerged as a less invasive alternative, offering reduced surgical risks and improved neurological outcomes. Electrophysiological tools routinely used for motor mapping in resective microsurgery are incompatible with intraoperative MRI. The utilization of advanced neuroimaging adjuncts for eloquent brain mapping during MRgLITT is imperative. The authors present the case of a 17-year-old athlete who underwent MRgLITT for a perirolandic long-term epilepsy-associated tumor (LEAT). They performed probabilistic multi-tissue constrained spherical deconvolution (MT-CSD) tractography to delineate the corticospinal tract (CST) for presurgical planning and intraoperative image guidance. The CST tractography was integrated into neuronavigation and MRgLITT workstation software to guide the ablation while monitoring the CST throughout the procedure. OBSERVATIONS: The integration of CST tractography into neuronavigation workstation planning and laser ablation workstation thermoablation is feasible and practical, facilitating complete ablation of a deep-seated perirolandic LEAT while preserving motor function. LESSONS: Probabilistic MT-CSD tractography enhanced MRgLITT planning as well as intraprocedural CST visualization and preservation, leading to a favorable functional outcome. The limitations of tractography and the predictability of thermal output distribution compared to the gold standard of microsurgical resection merit further discussion. https://thejns.org/doi/10.3171/CASE24139.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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