Rapid Diagnosis of Central Nervous System Neoplasms with Picosecond Infrared Laser Mass Spectrometry
Bibliographic record
Abstract
Surgery is the first step in the treatment of central nervous system (CNS) tumours. Thus, rapid, and precise intraoperative tissue diagnosis is imperative for surgical and adjuvant planning. In neurosurgery, however, preoperative biopsies are rare. Diagnosis depends on intraoperative consultations with neuropathologists, where excised tissue is evaluated via ‘frozen section’ analysis—which is limited by pathologist availability and expertise. This thesis addresses the need for a tool that provides fast, accurate CNS tumour diagnosis using both morphological and molecular data, enabling detailed surgical interventions. Picosecond InfraRed Laser Mass Spectrometry (PIRL-MS) is a hand-held, automated probe that identifies tumour subtypes through real-time molecular analysis. PIRL-MS is an ‘ambient’ mass spectrometry analysis method wherein rapid analysis of tissue molecular content takes place under ambient conditions without extensive sample preparation. Chapter 1 introduces a standardized workflow for developing and validating molecular models used in ambient mass spectrometry. It includes guidance on experimental design, model refinement, evaluation, and robustness assessment to support reproducible and interpretable tissue classification. Chapter 2 applies this framework to the intraoperative diagnosis of intradural extramedullary spinal tumours. A retrospective analysis of 319 specimens—primarily schwannoma and meningioma—revealed 41 lipid biomarkers through high-resolution tandem mass spectrometry liquid chromatography (HPLC-MS/MS), demonstrating that PIRL-MS can differentiate tumour types with high sensitivity and specificity. Chapter 3 expands this approach to glioma, lymphoma, and metastatic brain tumour specimens each of which requiring own extent (or aggressiveness) of surgical resection such that the workflow of intraoperative diagnosis is improved towards betterment of patient outcomes, even if no experienced pathologists is available to facilitate diagnosis. A lipid biomarker panel of 32 compounds was established for PIRL-MS-based classification. Additionally, murine models were used to show that PIRL-MS enables near-perfect tumour classification in situ, paving the way for human trials that could empower neurosurgeons to make accurate diagnoses independently, improving intraoperative decision-making. Overall, this work demonstrates the potential of ambient mass spectrometry—particularly PIRL-MS—for fast, reliable CNS tumour classification, bridging the gap between molecular diagnostics and real-time surgical needs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".