Predicting Progression of Low-Grade Oral Dysplasia Using Brushing-Based DNA Ploidy and Chromatin Organization Analysis
Bibliographic record
Abstract
Abstract Most oral cancers arise from oral potentially malignant lesions, which show varying grades of dysplasia. Risk of progression increases with increasing grade of dysplasia; however, risk prediction among oral low-grade dysplasia (LGD), that is, mild and moderate dysplasia can be challenging as only 5%–15% transform. Moreover, grading of dysplasia is subjective and varies with the area of the lesion being biopsied. To date, no biomarkers or tools are used clinically to triage oral LGDs. This study uses a combination of DNA ploidy and chromatin organization (CO) scores from cells obtained from lesion brushings to identify oral LGDs at high-risk of progression. A total of 130 lesion brushings from patients with oral LGDs were selected of which 16 (12.3%) lesions progressed to severe dysplasia or cancer. DNA ploidy and CO scores were analyzed from nuclear features measured by our in-house DNA image cytometry (DNA-ICM) system and used to classify brushings into low-risk and high-risk. A total of 57 samples were classified as high-risk of which 13 were progressors. High-risk DNA brushing was significant for progression (P = 0.001) and grade of dysplasia (P = 0.004). Multivariate analysis showed high-risk DNA brushing showed 5.1- to 8-fold increased risk of progression, a stronger predictor than dysplasia grading and lesion clinical features. DNA-ICM can serve as a non-invasive, high-throughput tool to identify high-risk lesions several years before transformation. This will help clinicians focus on such lesions whereas low-risk lesions may be spared from unnecessary biopsies. Prevention Relevance: DNA ploidy and chromatin organization of cells collected from oral potentially malignant lesions (OPMLs) can identify lesions at high-risk of progression several years prior. This non-invasive test would enable clinicians to triage high-risk (OPMLs) for closer follow-up while low-risk lesions can undergo less frequent biopsies reducing burden on healthcare resources.
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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 | 0.002 | 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) | 0.001 | 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".