Confocal fluorescence microscopy for detection of cervical preneoplastic lesions
Bibliographic record
Abstract
We examined and established the potential of ex-vivo confocal fluorescence microscopy for differentiating between normal cervical tissue, low grade Cervical Intraepithelial Neoplasia (CIN1), and high grade CIN (CIN2 and CIN3). Our objectives were to i) use Quantitative Tissue Phenotype (QTP) analysis to quantify nuclear and cellular morphology and tissue architecture in confocal microscopic images of fresh cervical biopsies and ii) determine the accuracy of high grade CIN detection via confocal microscopy. Cervical biopsy specimens of colposcopically normal and abnormal tissues obtained from 15 patients were evaluated by confocal fluorescence microscopy. Confocal images were analyzed and about 200 morphological and architectural features were calculated at the nuclear, cellular, and tissue level. For the purpose of this study, we used four features to delineate disease grade including nuclear size, cell density, estimated nuclear-cytoplasmic (ENC) ratio, and the average of three nearest Delaunay neighbors distance (3NDND). Our preliminary results showed ENC ratio and 3NDND correlated well with histopathological diagnosis. The Spearman correlation coefficient between each of these two features and the histopathological diagnosis was higher than the correlation coefficient between colposcopic appearance and histopathological diagnosis. Sensitivity and specificity of ENC ratio for detecting high grade CIN were both equal to 100%. QTP analysis of fluorescence confocal images shows the potential to discriminate high grade CIN from low grade CIN and normal tissues. This approach could be used to help clinicians identify HGSILs in clinical settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".