Picosecond Infrared Laser Mass Spectrometry Identifies a Metabolite Array for 10 s Diagnosis of Select Skin Cancer Types: A Proof-of-Concept Feasibility Study
Bibliographic record
Abstract
Currently, a large number of skin biopsies are taken for each true skin cancer case detected, creating a need for a rapid, high sensitivity, and specificity skin cancer detection tool to reduce the number of unnecessary biopsies taken from benign tissue. Picosecond infrared laser mass spectrometry (PIRL-MS) using a hand-held sampling probe is reported to detect and classify melanoma, squamous cell carcinoma, and normal skin with average sensitivity and specificity values of 86–95% and 91–98%, respectively (at a 95% confidence level) solely requiring 10 s or less of total data collection and analysis time. Classifications are not adversely affected by specimen’s quantity of melanin pigments and are mediated by a number of metabolic lipids, further identified herein as potential biomarkers for skin cancer-type differentiation, 19 of which were sufficient here (as a fully characterized metabolite array) to provide high specificity and sensitivity classification of skin cancer types. In situ detection was demonstrated in an intradermal melanoma mouse model wherein in vivo sampling did not cause significant discomfort. PIRL-MS sampling is further shown to be compatible with downstream gross histopathologic evaluations despite loss of tissue from the immediate laser sampling site(s) and can be configured using selective laser pulses to avoid thermal damage to normal skin. Therefore, PIRL-MS may be employed as a decision-support tool to reduce both the subjectivity of clinical diagnosis and the number of unnecessary biopsies currently required for skin cancer screening.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".