Advancements in Imaging and Neurosurgical Techniques for Brain Tumor Resection: A Comprehensive Review
Bibliographic record
Abstract
Brain tumor surgery has witnessed significant advancements over the past few decades, resulting in improved patient outcomes. Despite these advancements, brain tumors remain a formidable public health challenge due to their high morbidity and mortality rates. This review explores the evolution of neurosurgical techniques for brain tumor resection, emphasizing the balance between minimizing invasiveness and maximizing precision. Traditional approaches like craniotomy and keyhole surgery remain crucial, but the rise of minimally invasive techniques such as endoscopic endonasal surgery and laser interstitial thermal therapy (LITT) has revolutionized the field. Awake craniotomy has been a substantial stepping stone towards the preservation of neurological function among brain tumor patients. Additionally, the integration of brain mapping technologies including intraoperative MRI, ultrasound and fluorescence-guided surgery has enhanced the precision of tumor resections, particularly in eloquent brain areas. These innovations, while promising, also come with challenges, including steep learning curves and limited access to advanced technology in certain regions. As the field progresses, ongoing research is essential to refine these techniques and improve accessibility, ultimately aiming to increase survival rates and preserve neurological function in patients with brain tumors. The integration of advanced imaging techniques refined surgical tools, and artificial intelligence (AI) in surgical planning is expected to further improve the safety and effectiveness of neurosurgical procedures in the future. This review provides a comprehensive analysis of current surgical strategies and explores potential future directions in brain tumor surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 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) | 0.005 | 0.002 |
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".