Laser-Induced Thermal Therapy in the Management of Low-Grade Gliomas: A Narrative Review
Bibliographic record
Abstract
Background: Low-grade gliomas (LGGs) are slow-growing, World Health Organization Grade I and II tumors that can transform into more aggres-sive malignancies over time. This transformation pre- sents significant challenges in managing the burden of health care. Laser-induced thermal therapy (LITT) has emerged as a promising minimally invasive treat-ment option for LGGs, offering precise tumor ablation with minimal damage to surrounding tissues. Method: This narrative review synthesizes data from relevant studies on the evolution, clinical manifestations, mo-lecular characteristics, and emerging management strategies for LGGs, with a focus on the role of LITT. Results: LITT, a minimally invasive technique, offers targeted tumor ablation with the added benefit of disrupting the blood-brain barrier to enhance drug delivery. Studies have shown that LITT can effectively reduce tumor size and improve survival rates in patients with both primary and recurrent gliomas. However, challenges such as procedure-related complications, including motor deficits and cerebral edema, as well as the need for further research on long-term efficacy, remain. Conclusion: LITT represents a significant advancement in the treatment of LGGs, combining precision and minimal invasiveness. Future studies should focus on optimiz-ing protocols, integrating molecular and genetic in-sights, and assessing long-term outcomes to enhance therapeutic efficacy and patient quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".