ArticleFrontiers in oncology2026
HPV genotype-specific p16/Ki67 expression with machine learning-assisted assessment in cervical neoplasia.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Genotype-specific patterns of dual p16/Ki67 immunoexpression and their integration with computational assessment remain insufficiently delineated in cervical neoplasia. The present investigation characterized biomarker expression stratified by HPV genotype and evaluated the methodological feasibility of a deep learning-assisted scoring pipeline. Methods: A single-center cross-sectional investigation was conducted on 100 HPV-positive women, stratified into three categories: HPV16 mono-infection (n=33), non-HPV16 high-risk mono-infection (n=33), and multi-genotype co-infection (n=34). Whole-slide p16/Ki67 immunohistochemistry was scored through real-time consensus by two pathologists of differing experience levels, blinded to HPV genotype. A ResNet50-based computational pipeline was developed as a methodological feasibility demonstration and evaluated on an independent held-out test set (n=25). Between-group comparisons were performed using the Mann-Whitney Results: HPV16 mono-infection exhibited significantly elevated p16 immunoexpression (52.4 ± 27.6%) relative to non-HPV16 high-risk genotypes (31.1 ± 22.8%; Conclusions: HPV16 mono-infection is associated with distinctly elevated dual p16/Ki67 immunoexpression, providing methodological support for genotype-informed cytological risk stratification. The computational pipeline demonstrates technical feasibility; however, external multicenter validation is required prior to any consideration of clinical implementation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.