Pilot study: Raman spectroscopy in differentiating premalignant and malignant oral lesions from normal mucosa and benign lesions in humans
Bibliographic record
Abstract
BACKGROUND: Current practice for differentiating tissue lesions are based on histopathological criteria. This process is subject to error. The purpose of this study was to test whether an alternative, tissue-based molecular signatures Raman spectra could be used to differentiate premalignant and malignant lesions from normal mucosa or benign lesions. METHODS: Eighteen treatment naive subjects with histologically diagnosed oral disease were studied by comparing the Raman spectra of lesions with contralateral healthy sites. Principle component and multivariate analysis were used to predict which of the tissue groups the average spectrum of each lesion or normal tissue belonged. RESULTS: The average spectra were clearly different between premalignant and malignant lesions and those derived from normal, benign tissues. Premalignant and malignant lesions could be predicted with 100% sensitivity and 77% specificity. CONCLUSION: Raman spectroscopy (RS) offers the potential to provide point of care diagnosis of oral disease using a noninvasive, convenient, and relatively inexpensive technology.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".