Nuclear morphometry as a biomarker for bronchial intraepithelial neoplasia: Correlation with genetic damage and cancer development
Bibliographic record
Abstract
BACKGROUND: Bronchial carcinomas are preceded by epithelial morphologic changes. The variation in interpretation of these grades of intraepithelial neoplasia makes it difficult to determine its natural history and utility of histopathology as a surrogate endpoint biomarker. The objective of this study was to quantitate morphologic changes of intraepitherlial neoplasia and validate its utility through correlation with histopathology, allelic loss, and cancer development. METHODS: Quantitative nuclear morphometry was performed on 47 normal bronchial biopsies and 28 invasive cancer to generate a morphometry index (MI) that was applied to 1,096 bronchial biopsies from 230 volunteers who were current smokers (> or =25 pack-years) and 30 patients who had cancer. In a subset of 631 biopsies, MI was correlated with frequency of loss of heterozygosity at nine chromosomal regions (14 polymorphic markers). RESULTS: A significant correlation was found between MI and allelic loss in six of nine chromosomal regions. As part of patient long-term follow-up, 12 sites that progressed to cancer were identified and had significantly increased MIs relative to nonprogressing sites. Significant overlap in the MIs was found between most grades of intraepithelial neoplasia. CONCLUSIONS: In chemoprevention trials, nuclear morphometry can supplement histopathology as a Surrogate Endpont Biomarker (SEB) because it is quantitative, collelates well with genetic damage, and may predict cancer development.
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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 | 0.003 |
| 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 | 0.001 |
| 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 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".