Development and preclinical evaluation of an endonasal Raman spectroscopy probe for transsphenoidal pituitary adenoma surgery
Bibliographic record
Abstract
SignificanceFor most patients with pituitary adenomas, surgical resection represents a viable therapeutic option, particularly in cases with endocrine symptoms or local mass effects. Diagnostic imaging, including MRI and computed tomography, is employed clinically to plan pituitary adenoma surgery. However, these methods cannot provide surgical guidance information in real time to improve resection rates and reduce risks of damage to normal tissue during tumor debulking.AimHere, we present the development of a handheld Raman spectroscopy system that can be seamlessly integrated with transsphenoidal surgery workflows to allow live discrimination of all normal intracranial anatomical structures, including the pituitary gland, and potentially tissue abnormalities such as adenomas.ApproachA fiber-optic probe was developed with a form factor compatible with endoscopic systems for endonasal surgeries. The instrument was evaluated in an ex vivo experimental protocol designed to assess its ability to distinguish normal intracranial structures. A total of 274 in situ spectroscopic measurements were acquired from six lamb heads, targeting key anatomical structures encountered in surgery. Support vector machine models were developed to classify tissue types based on their spectral signatures.ResultsBinary classification models successfully distinguished the pituitary gland from other tissue structures with a sensitivity and a specificity of 100%. In addition, a four-class predictive model enabled >95% accuracy in situ discrimination of four structures of most importance during pituitary adenoma tumor resection, i.e., the pituitary gland, the sella turcica (ST) bone, the optic chiasm, and the ST dura mater.ConclusionsThis work sets the stage for the clinical deployment of Raman spectroscopy as an intraoperative real-time decision support system during transsphenoidal surgery, with future work focused on clinical integration and the generalization of the approach to include the detection of tissue abnormalities, such as pituitary adenomas.
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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 | 0.001 | 0.001 |
| 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".