5-Aminolevulinic Acid-Induced Porphyrin Contents in Various Brain Tumors: Implications Regarding Imaging Device Design and Their Validation
Bibliographic record
Abstract
BACKGROUND: Fluorescence-guided resections using 5-aminolevulinic acid (5-ALA)-induced tumor porphyrins have been established as an adjunct for malignant glioma surgery based on a phase III study using specifically adapted microscopes for visualizing fluorescing protoporphyrin IX (PPIX). New hardware technologies are being introduced, which claim the same performance as the original technology for visualizing fluorescence. This assumes that qualitative fluorescence detection is equivalent to the established standard, an assumption that needs to be critically assessed. OBJECTIVE: To determine PPIX concentrations (cPPIX) in tissue that can be detected visually using the established BLUE400 filter system (Carl Zeiss Meditec, Oberkochen, Germany) as a basis for defining the performance of this system. METHODS: Utilizing a hyperspectral imaging system, tumor samples from patients harboring different tumor tissues, with or without visible fluorescence, were analyzed. Absolute values of cPPIX were calculated after calibrating the system with fluorescence phantoms with known cPPIX. RESULTS: A total of 524 tumor samples from 162 patients were analyzed. Visual fluorescence under the BLUE400 filter was documented by experienced neurosurgeons. A 0.9 μg/ml threshold of cPPIX was defined as the minimal concentration required to detect and discriminate visual fluorescence. CONCLUSION: This is the first report providing data on the threshold of cPPIX, which is visually detected using the current generation of microscopes, thus defining the specificity and sensitivity of this technology as initially tested in a randomized trial. Novel technologies should show similar characteristics in order to be used safely and effectively. If more sensitive, such technologies require further assessments of tumor selectivity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".