Development of an endonasal Raman spectroscopy probe for pituitary adenoma surgery
Bibliographic record
Abstract
Surgical resection is a primary therapeutic option for parasellar tumors, including pituitary adenomas at the skull base. Endoscopic transsphenoidal surgery enables tumor removal through the nasal cavities employing endoscopic visualization and specialized instruments. Accurate identification and delineation of the tumor is challenging yet crucial, as the objective is to achieve gross total resection (GTR) while minimizing damage to the surrounding normal structures. Current intraoperative diagnostic modalities, such as MRI, CT, ultrasound probes, and fluorescence imaging have been clinically implemented; however, these techniques are not universally accessible, often require significant time and cost, or cannot deliver reliable real-time feedback. This study presents the development of a fiber-optic Raman spectroscopy probe specifically adapted for endonasal surgical applications to meet clinical needs. The instrument was designed to be integrated with current transsphenoidal surgery workflows, enabling real-time in situ interrogation of parasellar anatomical structures, including the pituitary gland. Before clinical deployment, the probe was evaluated in an ex vivo animal study designed to assess its ability to distinguish parasellar intracranial structures with in situ Raman spectroscopy measurements and machine learning-based classification. Preliminary results from a clinical study deployed at the Centre hospitalier de l'Université de Montréal (CHUM), including measurements acquired on 11 patients during tumor resections, revealed distinct Raman signatures and fluorescence variations across tissue and tumor types. Early SVM-based classification achieved encouraging accuracy rates, highlighting the potential of this approach, although further validation is necessary. Key challenges, such as interference from endoscopic light sources and blood contamination, were identified and addressed. This research sets the foundation for the clinical deployment of Raman spectroscopy as an intraoperative realtime decision support system for safer and more effective pituitary adenoma surgeries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".