Preliminary study demonstrating cancer cells detection at the margins of whole glioblastoma specimens with Raman spectroscopy imaging
Bibliographic record
Abstract
Intraoperative Raman spectroscopy uses near-infrared laser light to gain molecular information without causing damage. It can be used in vivo or ex vivo without exogenous contrast agents. Clinically, the technique was primarily used with machine learning for in situ tumor detection with fiberoptics probes analyzing tissue at sub-millimeter scales one point at the time. Here we report the development of a whole-specimen spectroscopic imaging system designed to detect cancer cells at the margins of surgical specimens. The system has a field of view covering a square area of side one centimeter with a pixel size of a quarter of a millimeter . First, a tumor detection model was developed from data acquired using a point-probe in 24 glioblastoma patients that had a detection sensitivity of 90% and a specificity of 95%. That model was then used to produce cancer prediction maps of nine glioblastoma specimens from five patients with validation based on histopathology analyses. The results preliminarily demonstrate the instrument was able to detect tissue areas associated with cancer cells from the Raman peaks associated with the amino acids phenylalanine and tryptophan as well as the relative concentration of lipids and proteins linked with deformations of the CH 2 and CH 3 bonds.
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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 | 0.000 |
| 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".