Exploratory analysis of quantitative histopathology of cervical intraepithelial neoplasia: Objectivity, reproducibility, malignancy‐associated changes, and human papillomavirus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: As part of a project to evaluate emerging optical technologies for cervical neoplasia, our group is performing quantitative histopathological analyses of biopsy specimens from 1,190 patients. Objectives in the interim analysis are (a) quantitatively assessing progression of the neoplastic process of cervical intraepithelial neoplasia (CIN)/squamous intraepithelial lesions (SIL), (b) detecting malignancy-associated changes (MACs), and (c) phenotypically measuring human papillomavirus (HPV) detected by DNA testing. METHODS: The diagnostic region of interest (ROI) from immediately adjacent sections were imaged, and the basal lamina and surface of the superficial layer were delimited. Nonoverlapping quantitatively stained nuclei were selected from 1,190 samples with histopathological characteristics of normal (929), koilocytosis (130), CIN 1 (40), CIN 2 (23), and CIN 3/carcinoma in situ (CIS) (68). A fully automatic procedure located and recorded the center of every nucleus in the region of interest (ROI). We used linear discriminant analysis to assess the changes between normal and CIN 3/CIS. RESULTS: Scores computed from the cell-by cell features and the clinical grade of CIN/SIL were highly correlated, as were those of the architectural features and the clinical grade of CIN/SIL. We found even higher correlations between a combination of cell-by-cell and architectural scores, and clinical grade. Using these scores, we found MACs in the normal biopsy specimens from patients with high-grade CIN/SIL. Furthermore, the same scores correlated with the molecular detection of HPV. CONCLUSIONS: Quantitative histopathology can be used in large clinical trials as an objective and reproducible measure of CIN/SIL. Detectable phenotypic changes correlate well with CIN/SIL neoplastic progression. It can also be used to infer the presence of CIN/SIL (MACs) and molecular changes associated with increased risk of cancer development (high-risk HPV).
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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 it