Human papillomavirus genotyping, human papillomavirus mRNA expression, and p16/Ki-67 cytology to detect anal cancer precursors in HIV-infected MSM
Bibliographic record
Abstract
OBJECTIVE: Anal cancer incidence is high in HIV-infected MSM. Screening for anal intraepithelial lesions and cancers is performed at specialized clinics and relies on high-resolution anoscopy (HRA) and anal cytology. Both approaches have limited reproducibility and sensitivity for detecting anal cancer precursors. We evaluated biomarkers for human papillomavirus (HPV)-related disease in a population of HIV-infected MSM. METHODS: A cross-sectional screening study with passive follow-up included 363 MSM followed at a HIV/AIDS clinic. All men had anal cytology samples taken and were evaluated using HRA and anal biopsies. Using a composite endpoint of biopsy results and cytology, we compared the performance of HPV16/18 genotyping, HPVE6/E7 mRNA expression, and p16/Ki-67 cytology to detect high-grade anal intraepithelial neoplasias (AINs). RESULTS: For all biomarkers analyzed, there was a significant trend of increasing percentage of men testing positive with increasing severity of disease (P < 0.001). HPV DNA testing had the highest sensitivity for anal intraepithelial neoplasia grade 2 and anal intraepithelial neoplasia grade 3 (AIN3), followed by p16/Ki-67, HPVE6/E7 mRNA testing, and HPV16/18 genotyping. The highest Youden's index was observed for HPVE6/E7 mRNA testing, followed by HPV16/18 genotyping, p16/Ki-67 cytology, and HPV DNA testing. Increasing the threshold for positivity of p16/Ki-67 to five or more positive cells led to significantly higher specificity, but unchanged sensitivity for detecting AIN3. CONCLUSION: Molecular features of anal disease categories are similar to those of corresponding cervical lesions. Biomarkers evaluated for cervical cancer screening may be used for primary anal cancer screening or to decide who should require immediate treatment vs. expectant management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".