Adapting Laser Interstitial Thermal Therapy for the Treatment of Naturally Occurring Intracranial Tumors in Dogs
Bibliographic record
Abstract
PURPOSE: Laser interstitial thermal therapy (LITT) is a minimally invasive surgical intervention permitting thermal ablation of intracranial targets such as tumors, radiation necrosis, or epileptogenic brain, including lesions that are deep, difficult to access, or recurrent that would otherwise have few viable surgical options. Despite its advantages, LITT has several limitations, including a restricted effective treatment zone (approximately 3 cm) and a limited ability to distinguish tumor margins from healthy brain tissue. Few viable animal models of appropriate size exist for studying LITT's impact on these disorders or for optimizing the technology and obviating its current limitations. Pet dogs develop these same disorders at similar rates to humans. We hypothesized that LITT could be made feasible in dogs, creating a unique model for in vivo LITT research and development. EXPERIMENTAL DESIGN: Canine cadaveric specimens and live dogs, including canine patients with spontaneously occurring intracranial gliomas, were used in this study. Commercially available equipment was used for neuronavigation (Curve, Brainlab) and to perform LITT (NeuroBlate, Monteris Medical). RESULTS: Canine cadavers and two end-of-life laboratory dogs allowed adaptation of the neuronavigation and LITT systems to dogs, with successful targeting and ablation of intracranial targets. Four canine patients with intracranial gliomas were subsequently successfully treated with these same technologies. CONCLUSIONS: This work establishes a unique canine model for in vivo LITT research and development using commercially available systems, as well as creating a viable cutting-edge therapeutic intervention for pet dogs with intracranial lesions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".