Laser interstitial thermal therapy for the treatment of insular lesions: A systematic review
Bibliographic record
Abstract
Background: The surgical treatment of insular lesions has been historically associated with high morbidity. Laser interstitial thermal therapy (LITT) has been increasingly used in the treatment of insular lesions, commonly neoplastic or epileptogenic. Stereotaxis is used to guide laser probes to the insula where real-time magnetic resonance thermometry defines lesion creation. There is an absence of previously published reviews on insular LITT, despite a rapid uptake in use, making further study imperative. Methods: Here we present a systematic review of the PubMed and Scopus databases, examining the reported clinical indications, outcomes, and adverse effects of insular LITT. Results: A review of the literature revealed 10 retrospective studies reporting on 53 patients (43 pediatric and 10 adults) that were treated with insular LITT. 87% of cases were for the treatment of epilepsy, with 89% of patients achieving seizure outcomes of Engle I-III following treatment. The other 13% of cases reported on insular tumors and radiological improvement was seen in all cases following treatment. All but one study reported adverse events following LITT with a rate of 37%. The most common adverse events were transient hemiparesis (29%) and transient aphasia (6%). One patient experienced an intracerebral hemorrhage, which required a decompressive hemicraniectomy, with subsequent full recovery. Conclusion: This systematic review highlights the suitability of LITT for the treatment of both insular seizure foci and insular tumors. Despite the growing use of this technique, prospective studies remain absent in the literature. Future work should directly evaluate the efficacy of LITT with randomized and controlled trials.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".