Abstract A18: Spatial dynamics of cellular proliferation in preneoplastic lesions: Comparison between bronchial, oral, and cervical epithelium
Bibliographic record
Abstract
Abstract Introduction: Uncontrolled proliferation is a hallmark of cancer and a biologically plausible risk biomarker for preneoplastic epithelium. The expression Ki-67 has been widely used as a marker of cellular proliferation even though it does not specifically elucidate the complexity of clonal growth that eventually drives a normal homeostatic epithelium towards carcinoma in situ. Our objective is to quantify the spatial dynamics of cellular proliferation in pre-neoplastic lesions of three different tissue types; bronchial, oral and cervical epithelium. Materials and Methods: Five hundred sixty-two bronchial lesions, seventy nine oral mucosa lesions, and two hundred sixty five cervical lesions were reviewed by at least two pathologists. Expression of Ki-67 was studied by Mib-1 immunohistochemistry. Using an in-house imaging system, a mapping of the regions of interest were performed; nuclei positions were registered; Mib-1 positive cells were manually marked, the basal membrane and the external surface delineated. Using graph-theory tools, the spatial arrangements of all nuclei, in addition to the spatial distribution of Mib-1 positive nuclei were measured. The percentage of Mib-1 positive nuclei within each layer - from the basal layer to the superficial layer - was a study focus. Results: On average, proliferation increased with pathology grade. Nevertheless, the amplitude and the patterns of cellular proliferation within the different layers differ among the different tissue types. The propotions of Mib-1 positive cells in the bronchial, oral, and cervical normal epithelium were respectively: 19.8%, 6.3%, and 6.1% in the basal layer; 18.7%, 28.5% and 33.2 in the layer 1; 8.0%, 18.7% and 31.1% in the layer 2. For specimens classified as mild dysplasia, the proportion of Mib-1 positive cells in bronchial, oral and cervical dysplastic epithelium were respectively: 42.0%, 19.3%, and 21.5% in the basal layer; 63,2%, 19.3%, and 43.5% in layer 1; 61.8%, 9.9% and 50.0% in layer 2. Furthermore, we observed a high variability of the proliferation patterns (along the different layers) in each tissue within each pathology grade. The significance of these findings and their correlation with other biomarkers (quantitative nuclear phenotype, p16 staining, LOH, etc.) and with clinical parameters (age, sex, HPV status, cancer progression, etc..) will be shown. Conclusions: Quantitative analysis of spatial patterns and arrangement of proliferating cells squamous dysplastic lesions provide additional insights in the dynamic nature of these early neoplastic changes. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A18.
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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 | 0.001 | 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 | 0.001 |
| 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".