P05.26.B FLUOROPHORE ABUNDANCE VERSUS HISTOLOGICAL CELLULARITY IN FLUORESCENCE-GUIDED GLIOMA SURGERY
Bibliographic record
Abstract
Abstract BACKGROUND Diffuse infiltration of malignant glioma cells into surrounding brain tissue complicates maximal safe resection. Fluorescence-guided surgery (FGS) with 5-aminolevulinic acid (5-ALA) enhances tumor visualization via protoporphyrin IX (PpIX) fluorescence, but its relationship to cell density remains unclear. We investigate whether PpIX fluorescence correlates with histological cellularity in glioma tissues. MATERIAL AND METHODS We analyzed 243 brain tumor biopsies from patients administered with 5-ALA. Ex vivo hyperspectral imaging captured fluorescence spectra, and a spectral unmixing algorithm quantified abundances of nine fluorophores, including PpIX. Cell density was measured from histopathological slides using an automated cell counting algorithm involving image segmentation and morphological filtering. We focused on glioblastoma samples (n=209) to control for tissue-type variability. For comparison, other tumor types included anaplastic astrocytoma (n=10), gliosarcoma (n=9), metastasis (n=5), and radiation necrosis (n=10). Using linear and quadratic models, we assessed correlations between fluorophore abundances and cell density. RESULTS In glioblastoma samples, weak but statistically significant positive correlations were found between cell density and fluorescence from PpIX634 (R=0.387, p<0.001) and collagen (R=0.403, p<0.001), with collagen showing a slightly stronger correlation. No strong correlation was observed between PpIX fluorescence intensity and cell density across all tumor types (R²=0.17, p<0.001). Quadratic models marginally improved the correlation (R²=0.28) but risked overfitting. Tissue type significantly influenced cellularity and fluorophore abundance (p<0.05), suggesting that factors beyond cell density affect PpIX accumulation. Glioblastoma samples exhibited higher PpIX fluorescence despite lower cell densities than other tumor types, indicating PpIX accumulation in the extracellular matrix or higher intracellular accumulation per cell. CONCLUSION PpIX fluorescence correlates weakly with tumor cell density, suggesting accumulation in the extracellular matrix rather than within tumor cells or higher intracellular accumulation per cell. Collagen fluorescence shows a stronger correlation and may serve as an additional intraoperative biomarker. While PpIX aids tumor visualization, it does not directly reflect cell density. Incorporating biomarkers like collagen fluorescence could enhance tumor delineation and improve surgical outcomes.
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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 | 0.002 |
| 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) | 0.002 | 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".