SURG-28NEUROSURGICAL INTRAOPERATIVE SPECTROSCOPY: TOWARDS REAL-TIME CNS TUMOUR BIOPSIES
Bibliographic record
Abstract
Immediate, real-time accurate tissue characterisation is a crucial unmet need during surgery for the maximal resection of CNS tumours whilst causing minimum damage to surrounding functioning brain tissue. We describe the first ever use of mass spectrometry and Raman spectroscopy to immediately characterize brain tumours during surgery with accuracy rivaling that of traditional histopathological techniques. A prospective observational study was designed consisting of 40 patients undergoing Neurosurgical resection of a range of WHO grade I-IV brain tumours and metastases. A Neuronavigational platform was devised combining advanced spectrometry technologies together with intraoperative imaging to allow accurate 3D spectral acquisition during surgery taking into account brain shift. The resulting data from 40 cases consists of over 1,500 brain spectra acquired during surgery with high spatial localization. Analysis shows unique spectra for each type of tumour with sensitivity and specificity comparable to the current gold standard of histopathological analysis. The spatial heterogeneity of tumour spectra sheds important light into underlying genomic and metabolomic profiles of the tumours studied including IDH-1 mutation and MGMT status. We have demonstrated that the use of molecular data with high spatial resolution obtained through mass spectral and Raman spectral analysis shows that real-time biopsies are obtainable and represent an invaluable resource in the decision making process during surgery. This also represents an opportunity to fast track vital post surgical therapies currently offered to high grade brain tumours and investigate novel aspects of in-vivo tumour biology through a new modality with the possibility of novel biomarker discovery. This sets the stage for a formal clinical trial looking into the benefits of spectral guided resection in terms of safer and more complete resection, rapid progress to further treatment and improved overall survival.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".