{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T05:05:34Z","timestamp":1780031134042,"version":"3.53.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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\u2013assisted scoring pipeline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      test with Bonferroni correction; multivariable logistic regression and receiver operating characteristic (ROC) analysis were employed to quantify biomarker discriminative performance; and analysis of covariance (ANCOVA) with age as a continuous covariate was undertaken as a pre-specified sensitivity analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      HPV16 mono-infection exhibited significantly elevated p16 immunoexpression (52.4 \u00b1 27.6%) relative to non-HPV16 high-risk genotypes (31.1 \u00b1 22.8%;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.0021; Cohen\u2019s\n                      <jats:italic>d<\/jats:italic>\n                      \u00a0=\u00a00.844) and co-infections (34.6 \u00b1 24.1%); between-group differences persisted following age adjustment. On the independent test set, the computational model yielded an accuracy of 96.0% (95% CI, 78.3\u201399.9%) and an AUC-ROC of 0.96. Given the restricted test set dimension and single-center design, these metrics should be construed as preliminary.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1819567","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T04:55:50Z","timestamp":1780030550000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HPV genotype-specific p16\/Ki67 expression with machine learning-assisted assessment in cervical neoplasia"],"prefix":"10.3389","volume":"16","author":[{"given":"Meryem","family":"Kececi Oguzhanoglu","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Haseki Training and Research Hospital, University of Health Sciences","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esen Gul","family":"Uzuner","sequence":"additional","affiliation":[{"name":"Department of Pathology, Haseki Training and Research Hospital, University of Health Sciences","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kursat","family":"Oguzhanoglu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Haseki Training and Research Hospital, University of Health Sciences","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Cetin","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Haseki Training and Research Hospital, University of Health Sciences","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senem","family":"Karacabey Cakmak","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Haseki Training and Research Hospital, 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