INNV-24. MACHINE LEARNING-BASED SPECTROSCOPIC TISSUE DIFFERENTIATION IN FLUORESCENCE-GUIDED NEUROSURGERY
Bibliographic record
Abstract
Abstract Maximal resection of malignant gliomas is hindered by difficulty in distinguishing tumor margins. Fluorescence-guided resection with 5-ALA assists in reaching this goal. Previously, we characterized five fluorophore emissions that accurately represent any spectrum measured from human brain tumor biopsies with a wide-field hyperspectral device. In this study, the effectiveness of these five spectra was explored for different tumor classification tasks in 891 hyperspectral widefield measurements of 184 patients harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8), which corresponds to up to 15000 spectra for a given test. The statistical differences in fluorophore abundances between classes were determined and visualized using dimensionality reduction techniques, including principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). Three machine-learning models were trained to classify tumor type (12 classes), grade (3 classes), and glioma margins (3 classes). Five algorithms were tested with varying hyperparameters for each classification task. We explored whether PCA projection onto five different axes than fluorophore abundances can provide more information for visualization and classification. The abundances of the five a priori fluorophore spectra matched or outperformed the five optimal PCA components for all classification tasks. These five axes capture 96-99% of the variance in the dataset. Using random forests and multilayer perceptrons, the classifiers achieved average test accuracies of 74-82%, 79%, and 81%, respectively. All five fluorophore abundances varied between tumor margin type as well as between grades (p < 0.01). For tissue type, at least four of five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the differing contribution of fluorophores in different tissue classes tissue, as well as the five fluorophores value as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".