Magnetic Resonance Imaging-Guided Focused Laser Interstitial Thermal Therapy for Subinsular Metastatic Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND AND IMPORTANCE: To describe the novel use of the AutoLITT System (Monteris Medical, Winnipeg, Manitoba, Canada) for focused laser interstitial thermal therapy (LITT) with intraoperative magnetic resonance imaging (MRI) and stereotactic image guidance for the treatment of metastatic adenocarcinoma in the left insula. CLINICAL PRESENTATION: The patient was a 61-year-old right-handed man with a history of metastatic adenocarcinoma of the colon. He had previously undergone resection of multiple lesions, Gamma Knife radiosurgery, and whole-brain radiation. Despite treatment of a left insular tumor, serial imaging revealed that the lesion continued to enlarge. Given the refractory nature of this tumor to radiation and the deep-seated location, the patient elected to undergo LITT treatment. The center of the lesion and entry point on the scalp were identified with STEALTH (Medtronic, Memphis, Tennessee) image-guided navigation. The AXiiiS Stereotactic Miniframe (Monteris Medical) for the LITT system was secured onto the skull, and a trajectory was defined to achieve access to the centroid of the tumor. After a burr hole was made, a gadolinium template probe was inserted into the AXiiiS base. The trajectory was confirmed via an intraoperative MRI, and the LITT probe driver was attached to the base and CO2-cooled, side-firing laser LITT probe. The laser was activated and thermometry images were obtained. Two trajectories, posteromedial and anterolateral, produced satisfactory tumor ablation. CONCLUSION: LITT with intraoperative MRI and stereotactic image guidance is a newly available, minimally invasive, and therapeutically viable technique for the treatment of deep seated brain tumors.
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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 | 0.000 | 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 | 0.000 |
| 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 | 0.001 |
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".