Exploratory analysis of quantitative histopathology of cervical intraepithelial neoplasia: Objectivity, reproducibility, malignancy‐associated changes, and human papillomavirus
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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How this classification was reachedexpand
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 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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".