Fluorescence real-time kinetics of protoporphyrin IX after 5-ALA administration in low-grade glioma
Bibliographic record
Abstract
OBJECTIVE: 5-Aminolevulinic acid (5-ALA) induces fluorescence in high-grade glioma (HGG), which is used for resection. However, the value of 5-ALA-induced fluorescence in low-grade glioma (LGG) is unclear. Time dependency and time kinetics have not yet been investigated. The purpose of this study was to investigate real-time kinetics of protoporphyrin IX (PpIX) in LGG based on hyperspectral fluorescence-based measurements and identify factors that predict fluorescence. METHODS: Patients with grade II gliomas and imaging from which HGGs could not be completely ruled out received 5-ALA at 20 mg/kg body weight 4 hours prior to surgery. Fluorescence intensity (FI) and PpIX concentration (CPpIX) were measured in tumor tissue utilizing a hyperspectral camera. Apparent diffusion coefficient (ADC)-based tumor cell density, Ki-67/MIB-1 index, chromosomal 1p/19q codeletion, and 18F-fluoroethyl-l-tyrosine (18F-FET) PET values and their role for predicting fluorescence were evaluated. RESULTS: Eighty-one biopsies from 25 patients were included. Tissues with fluorescence demonstrated FI and CPpIX maxima between 7 and 8 hours after administration. When visible fluorescence was observed, peaks of FI and CPpIX were observed within this 7- to 8-hour time frame, regardless of any MRI gadolinium contrast enhancement. Gadolinium enhancement (p = 0.008), Ki-67/MIB-1 index (p < 0.001), 18F-FET PET uptake ratio (p = 0.004), and ADC-based tumor cellularity (p = 0.017) significantly differed between fluorescing and nonfluorescing tissue, but not 1p/19q codeletions. Logistic regression demonstrated that 18F-FET PET uptake and Ki-67/MIB-1 index were independently related to fluorescence. CONCLUSIONS: This study reports a fluorescence-based assessment of CPpIX in human LGG tissues related to 18F-FET PET uptake and Ki-67/MIB-1. As in HGGs, fluorescence in LGGs peaked between 7 and 8 hours after 5-ALA application, which has consequences for the timing of administration.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".