Abstract 5932: Molecular determinants of 10-second diagnosis of brain cancer types with laser mass spectrometry
Bibliographic record
Abstract
Abstract Surveys of neurosurgical outcome data have suggested suitable extents (aggressiveness) of resection to improve survival for many brain cancer types. Here, personalization of the extent of resection relies on highly subjective intraoperative diagnoses wherein the depth of the diagnostic information depends on the level of experience of the pathologist. Picosecond InfraRed Laser Mass Spectrometry (PIRL-MS) uses picosecond bursts of mid- infrared laser radiation to extract, in a non-thermal manner, tissue lipids (1) present in cellular membranes, giving cells and nuclei their unique shapes (utilized in morphometric pathology with staining & microscopy) and (2) altered in tumorigenesis due to cross-talk with cellular signalling in metabolism (potentially also revealing cancer molecular subtypes in 10 seconds as shown) for real-time profiling of molecular content unique to each tumour type. In our laboratory, close to 1, 300 frozen brain cancer specimens over 30 different classes of adult and pediatric cancers are being subjected PIRL-MS to build a comprehensive molecular signature library using various supervised and unsupervised dimensionality reduction and data analysis methods. The sensitivity and specificity of the integrated morphometric and molecular diagnosis for pediatric brain cancers with 10-second PIRL-MS was > 96%. This classification could use as little as only 18 tissue lipids whose identities were determined using chromatography and tandem mass spectrometry. Greater than 90% sensitivity and specificity have been obtained for adult brain cancer classifications with 10-second PIRL-MS analysis utilizing 40 lipids spanning fatty acids, phospholipids and ceramides. PIRL-MS can provide non-subjective differentiation between various brain cancer types with as little as 10 seconds of sampling and analysis time. Citation Format: Alexa Fiorante, Michael Woolman, David Munoz, Lan Anna Ye, Taira Kiyota, Sunit Das, Ahmed Aman, Howard Ginsberg, Gelareh Zadeh, Arash Zarrine-Afsar. Molecular determinants of 10-second diagnosis of brain cancer types with laser mass spectrometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5932.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".