Value of CCNA1 promoter methylation in triaging ASC-US cytology
Bibliographic record
Abstract
BACKGROUND: Using HPV testing to triage ASC-US still has some problems of unnecessary colposcopy in many cases. A previous study reported that methylation of CCNA1, a tumor suppressor gene, can differentiate between low and high grade lesions. This study was designed to evaluate the diagnostic values and application of CCNA1 methylation in the patients with ASC-US group. Materials and methods: Cross sectional analytic study was conducted in the patients with ASC-US cytology. HPV DNA testing and CCNA1 promoter methylation testing were performed. The patients were sent for colposcopic examination and biopsy. Biopsy results were considered as gold standard. Diagnostic test of HPV test and CCNA1 methylation test were calculated for sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), likelihood ratio for test positive and negative and 95% confidence interval. Results: One hundred and seventy patients were enrolled. Mean age was 39.7 years old. HR-HPV was positive in 70% of the patients. HPV type 16, type 18 and non-16,18 were 12.4%, 4.7% and 42.4%, respectively. CIN2+ were found in 12.4% (21 cases). CCNA1 promoter methylation was positive in 5 cases. CCNA1 had high specificity 99.3%, NPV 89.2% and PPV 80% in detection of CIN2+ but sensitivity was 19%. Likelihood ratio for positive test was 28.4 and likelihood ratio for negative test was 0.8. HPV test had sensitivity of 90.5% and NPV of 95.9% but low specificity and PPV as 31.5% and 15.7%, respectively. Conclusion: CCNA1 promoter methylation testing had very high specificity, likelihood ratio for the positive test and PPV (99.3%, 28.4 and 80.0, respectively). Therefore, CCNA1 promoter methylation test may be used in the HPV DNA positive cases to classify the urgency of colposcopy and the colposcopist should pay more attention to CCNA1 positive patients because of their higher chance to identify the significant lesions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".