Increased brain tumor resection using fluorescence image guidance in a preclinical model
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Fluorescence image-guided brain tumor resection is thought to assist neurosurgeons by visualizing those tumor margins that merge imperceptibly into normal brain tissue and, hence, are difficult to identify. We compared resection completeness and residual tumor, determined by histopathology, after white light resection (WLR) using an operating microscope versus additional fluorescence guided resection (FGR). STUDY DESIGN/MATERIALS AND METHODS: We employed an intracranial VX2 tumor in a preclinical rabbit model and a fluorescence imaging/spectroscopy system, exciting and detecting the fluorescence of protoporphyrin IX (PpIX) induced endogenously by administering 5-aminolevulinic acid (ALA) at 4 hours before surgery. RESULTS: Using FGR in addition to WLR significantly increased resection completeness by a factor 1.4 from 68+/-38 to 98+/-3.5%, and decreased the amount of residual tumor post-resection by a factor 16 from 32+/-38 to 2.0+/-3.5% of the initial tumor volume. CONCLUSIONS: Additional FGR increased completeness of resection and enabled more consistent resections between cases.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".