Characterization of autofluorescence and quantitative protoporphyrin IX biomarkers for optical spectroscopy-guided glioma surgery
Bibliographic record
Abstract
Abstract 5-Aminolevulinic acid (5-ALA)-mediated fluorescence does not effectively depict low grade gliomas (LGG) or the infiltrative tumor portion of high-grade gliomas (HGG). While spectroscopy improves sensitivity and precision, this is currently limited by autofluorescence and a second protoporphyrin IX (PpIX) fluorescence state at 620 nm. We investigated the autofluorescence to better characterize the present spectra and thus increase PpIX quantification precision and sensitivity. This study included 128 patients undergoing surgery for malignant glioma. 5-ALA (Gliolan) was administered before anesthesia, and fluorescence was measured using a hyperspectral device. It was found that all 2692 measured spectra consisted of contributions from 620 to 634 nm PpIX, NADH, lipofuscin, and flavins. The basis spectra were characterized and their use in spectral unmixing led to 82.4% lower fitting error for weakly fluorescing areas ( p < 0.001), and 92.3% fewer false positive tumor identifications in control measurements ( p = 0.0065) compared to previous works. They also decreased the PpIX 620 contribution, thus halving the mean Ratio 620/634 ( p < 0.001). The ratio was approximately 0 for HGGs and increasing for LGGs, as demonstrated previously. Additionally, the Ratio 620/634 , the MIB-1/Ki-67 proliferation index, and the PpIX peak blue-shift were found to be significantly related to WHO grade, fluorescence visibility, and PpIX contribution ( p < 0.001), and the value of these three as quantitative biomarkers is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".