Incorporating patient demographics into Raman spectroscopy algorithm improves in vivo skin cancer diagnostic specificity
Bibliographic record
Abstract
Abstract The study objective is to evaluate whether incorporating patient demographics into Raman spectral analysis can improve diagnostic performance. In vivo Raman spectra of 731 cases were analyzed by dividing the data into two groups: skin cancers/precancers (malignant melanoma, basal cell carcinoma, squamous cell carcinoma, and actinic keratosis, n = 340) and benign lesions (pigmented nevi and seborrheic keratosis, n = 391). Patient age, gender, skin type and location of the lesion were taken into account in the analysis. Multivariate statistical analysis including principal component and general discriminant analysis and partial least squares (PLS) were utilized for lesion discrimination. Based on PLS analysis, the area under receiver operating characteristic curve was improved from 0.913 to 0.934 ( P < .05) after incorporating patient demographics into the algorithm; the specificity was increased from 33.5% to 44.5%, 56.0% to 68.5% and 76.0% to 82.1% for sensitivity of 99%, 95% and 90%, respectively ( P < .05 for all sensitivity levels).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".