Picosecond InfraRed Laser-Mass Spectrometry for Rapid Detection of Skin Cancers
Bibliographic record
Abstract
Skin cancer is the most common cancer known to humans, worldwide. Current detection and treatment of skin cancers remains challenging and time-consuming, as detection relies on stepwise protocols involving visual inspection, optical inspection by a device known as a dermoscopy, and subsequent excision or punch biopsy for histopathological analysis. Furthermore, cutaneous melanoma (melanoma of the skin) can visually mimic other cutaneous lesions, leading to some degree of difficulty in classification of melanoma subtypes, necessitating a large number of biopsies to be taken. Various studies have indicated 24 out of every 25 biopsies performed for a suspected melanoma are confirmed to be benign post-biopsy, leading to a large amount of unnecessary biopsies. There thus exists a need for development of novel detection technologies for more rapid, less subjective, yet highly specific characterization of skin cancers at the pre-biopsy stage.Mass spectrometry (MS) is an analytical technique becoming increasingly important in clinical research. MS allows for tissue identification and disease site categorization based on molecular characterization of tissues. Through addition of an ambient ionization laser source known as a Picosecond InfraRed Laser (PIRL), rapid and accurate classifications of skin cancers may be realized. Throughout the research presented in this thesis on banked ex vivo human skin cancers as well as in murine models, once a mass spectrometry molecular signature model has been built from a library of known samples, PIRL-MS may be used to diagnose unknown skin cancers rapidly, within just 5-10 seconds of sampling and analysis time with high sensitivity and specificity. In this thesis, I apply PIRL-MS to skin cancers to enable for rapid, sensitive and specific detection of skin cancers prior to the biopsy stage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.003 |
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".