5-Aminolevulinic Acid-Induced Protoporphyrin IX Fluorescence in Meningioma
Bibliographic record
Abstract
BACKGROUND: The use of 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence has shown promise as a surgical adjunct for maximizing the extent of surgical resection in gliomas. To date, the clinical utility of 5-ALA in meningiomas is not fully understood, with most descriptive studies using qualitative approaches to 5-ALA-PpIX. OBJECTIVE: To assess the diagnostic performance of 5-ALA-PpIX fluorescence during surgical resection of meningioma. METHODS: ALA was administered to 15 patients with meningioma undergoing PpIX fluorescence-guided surgery at our institution. At various points during the procedure, the surgeon performed qualitative, visual assessments of fluorescence by using the surgical microscope, followed by a quantitative fluorescence measurement by using an intraoperative probe. Specimens were collected at each point for subsequent neuropathological analysis. Clustered data analysis of variance was used to ascertain a difference between groups, and receiver operating characteristic analyses were performed to assess diagnostic capabilities. RESULTS: Red-pink fluorescence was observed in 80% (12/15) of patients, with visible fluorescence generally demonstrating a strong, homogenous character. Quantitative fluorescence measured diagnostically significant PpIX concentrations (cPpIx) in both visibly and nonvisibly fluorescent tissues, with significantly higher cPpIx in both visibly fluorescent (P < .001) and tumor tissue (P = .002). Receiver operating characteristic analyses also showed diagnostic accuracies up to 90% for differentiating tumor from normal dura. CONCLUSION: ALA-induced PpIX fluorescence guidance is a potential and promising adjunct in accurately detecting neoplastic tissue during meningioma resective surgery. These results suggest a broader reach for PpIX as a biomarker for meningiomas than was previously noted in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".