Oral cancer detection and progression prediction using noninvasive cytology‐based DNA ploidy approach
Bibliographic record
Abstract
BACKGROUND: Despite the oral cavity being readily accessible, oral cancer (OC) remains a significant burden. The objective of this study is to develop a DNA ploidy-based cytology test for early detection of high-risk oral lesions. METHODS: This retrospective study was conducted using 569 oral brushing samples collected from 95 normal and 474 clinically abnormal mucosa with biopsy diagnosis of reactive, low-grade or high-grade precancer or cancers. Brushing cells were processed to characterize DNA ploidy. A two-step DNA ploidy-based algorithm, the DNA ploidy oral cytology (DOC) test, was developed using a training set, and verified in test and validation sets to differentiate high-grade lesions (HGLs) from normal. The prognostic value of the test was evaluated by an independent outcome cohort, including progressed and non-progressing normal, reactive and low-grade lesions. Classification performance was assessed by accuracy, sensitivity, and specificity, while the prognostic value was evaluated by using the Cox proportional hazards analysis on 3-year progression-free survival (PFS). RESULTS: The developed DOC test exhibited high accuracy for detecting HGLs in the test and validation sets, with a sensitivity of 0.97 and 0.96, respectively. Its application to the Outcome cohort demonstrated significant prognostic value for 3-year PFS (log rank, p < 0.001). Multivariate analysis showed that high-grade pathology was the only variable explaining positive DOC test, not age, smoking, or lesional site. CONCLUSION: Clinical implementation of the DOC test could provide an effective screening method for detecting HGLs for biopsy and lesions at risk of progression.
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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".