504 Improving Precision of Resection Limits in Surgery for High-grade Gliomas: Preliminary Experience with an In Vivo Raman Spectroscopy Probe
Bibliographic record
Abstract
INTRODUCTION: Supramaximal resection of gliomas to anatomic-functional boundaries is currently widely employed. This leads to removal of potentially tumor-negative thus healthy brain tissue. Therefore, a device that detects glioma tissue infiltrating the white matter in vivo would be valuable. METHODS: Patients and methods. An in vivo Raman spectroscopic system (Sentry 1000, ODS Medical, Montreal, Canada) with an early stage binary classifier trained on 501 samples from 17 patients (tumor n=211 and pure normal n=290 from Montreal Neurological Hospital, Montreal, Canada) has been employed in 15 patients with high-grade gliomas . Of those, 172 samples were collected from 2 areas: n=18 from the central tumor core and n=154 from the adjacent glioma-infiltrated white matter until Raman negative or until an anatomic or functional boundary was reached. All samples were reviewed for the presence of glioma cells by histopathology. RESULTS: Sensitivity of Raman spectroscopy for correct high-grade glioma cells was 79%, specificity for non-infiltrated white matter was 52%. Median resection volume outside MR contrast enhancement was 78 ± 143 cm 3 . Removal of the infiltration zone guided by Raman spectroscopy revealed a non-concentric growth mainly along white matter fiber bundles. CONCLUSION: According to our data, Raman spectroscopy has the potential to improve the precision of high-grade glioma resection. This may allow a more selective removal of the tumor-infiltrated white matter.
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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) | 0.001 | 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) | 0.001 | 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".