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This model will help the early detection and focused screening of individuals at increased risk of dVIN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The clinical data from 257 patients diagnosed with dVIN or HSIL were retrospectively reviewed. Patients were divided into two distinct cohorts: relapse (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200960) and non-recurrence (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009197). For robust model development, the dataset was methodically divided into two subsets: training (70% of cases) and validation (30% of cases). Logistic regression analysis was applied to identify key predictors. Subsequently, they were combined to construct a risk prediction model for post-treatment recurrence of HSIL and dVIN.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Univariate logistic regression analysis revealed that age, menopause, immunosuppression, HPV16 infection, histopathological characteristics, and positive surgical margins were positively correlated with recurrence risk. In contrast, low-risk HPV types exhibited a negative correlation with recurrence risk (all\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). Multivariable logistic regression analysis revealed that age, smoking history, immunosuppression, HPV16 infection, and histopathology were robustly associated with an increased recurrence risk (all\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). In the training dataset, the area under the receiver operating characteristic curve (AUC) was 0.793 (95% CI 0.77\u20130.880), accompanied by a median prediction success probability of 0.810. In the internal validation dataset, the AUC improved to 0.831 (95% CI 0.726\u20130.937). The Hosmer\u2013Lemeshow goodness-of-fit test revealed acceptable model calibration for the training (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.069) and internal validation (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.086) sets. Calibration curves revealed trends remarkably consistent with the ideal curve, indicating commendable calibration. Furthermore, clinical decision curve analysis substantiated the net benefit of the model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A prediction model including age, smoking history, immunosuppression, HPV16 infection, and histopathology exhibits good predictive performance for post-treatment recurrence of HSIL and dVIN. Our model can help clinicians assess recurrence risk, providing guidance for clinical consultations and enabling targeted follow-up and treatment plans for individuals at high risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12672-026-04714-w","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:53:11Z","timestamp":1771487591000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a risk assessment model for the recurrence of high-grade squamous intraepithelial lesions and differentiated vulvar intraepithelial neoplasia in Chinese cohort"],"prefix":"10.1007","volume":"17","author":[{"given":"Xi","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangfeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenmin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanxuan","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liehong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"issue":"5","key":"4714_CR1","first-page":"409","volume":"72","author":"S Rebecca","year":"2022","unstructured":"Rebecca S, Kimberly M, Leticia N, Theresa D, Angela M, Robin Y, et al. 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