Development and first in‐human use of a Raman spectroscopy guidance system integrated with a brain biopsy needle
Bibliographic record
Abstract
Navigation-guided brain biopsies are the standard of care for diagnosis of several brain pathologies. However, imprecise targeting and tissue heterogeneity often hinder obtaining high-quality tissue samples, resulting in poor diagnostic yield. We report the development and first clinical testing of a navigation-guided fiberoptic Raman probe that allows surgeons to interrogate brain tissue in situ at the tip of the biopsy needle prior to tissue removal. The 900 μm diameter probe can detect high spectral quality Raman signals in both the fingerprint and high wavenumber spectral regions with minimal disruption to the neurosurgical workflow. The probe was tested in three brain tumor patients, and the acquired spectra in both normal brain and tumor tissue demonstrated the expected spectral features, indicating the quality of the data. As a proof-of-concept, we also demonstrate the consistency of the acquired Raman signal with different systems and experimental settings. Additional clinical development is planned to further evaluate the performance of the system and develop a statistical model for real-time tissue classification during the biopsy procedure.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".