{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T07:21:01Z","timestamp":1789802461470,"version":"4.0.1"},"reference-count":23,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J011218"],"award-info":[{"award-number":["2023J011218"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020783","name":"Fujian Provincial Federation of Social Sciences","doi-asserted-by":"publisher","award":["FJ2023C082"],"award-info":[{"award-number":["FJ2023C082"]}],"id":[{"id":"10.13039\/501100020783","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Cervical intraepithelial neoplasia grade 2 (CIN2) represents a critical turning point in cervical cancer progression. While its natural regression rate reaches 50%\u201357%, CIN2 can also progress to higher\u2010grade lesions or cervical cancer. Accurately predicting CIN2 outcomes is essential for individualized management and avoiding overtreatment. The present study aimed to develop a deep learning\u2013based prognostic model using colposcopic images to predict CIN2 outcomes: regression, persistence, and progression, and to provide clinically meaningful risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Colposcopic images from 212 patients diagnosed with CIN2 were retrospectively collected, including unstained, acetic acid\u2010stained, and Lugol's iodine\u2010stained images. Region of interest extraction and cluster analysis were employed for feature quantification. Four machine learning models (logistic regression, XGBoost, Random Forest and Extra Trees) were constructed to predict outcomes. Performance was evaluated using confusion matrices, receiver operating characteristic (ROC) curves, precision\u2010recall curves, and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 212 patients, 52.4% regressed, 31.1% persisted, and 16.5% progressed. Automated pipeline generated 42\u201345 informative image clusters per staining type. For unstained images, logistic regression performed best (macro\u2010AUC 0.884); for acetic acid\u2010stained images, XGBoost achieved the highest accuracy (macro\u2010AUC 0.933) with 70.0% sensitivity for progression; for iodine\u2010stained images, Extra Trees showed the highest regression sensitivity (93.9%). Decision curve analysis confirmed clinical utility, and SHAP analysis highlighted prognostic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning models based on colposcopic images can effectively predict CIN2 prognosis, offering an objective tool for risk assessment and individualized clinical management. Different staining modalities provide complementary information, aiding in balancing progression risk reduction with overtreatment avoidance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70976","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:52:21Z","timestamp":1774515141000},"page":"1547-1558","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated acquisition of explainable knowledge from unannotated colposcopic images for predicting the natural course of\n                    <scp>CIN2<\/scp>"],"prefix":"10.1002","volume":"174","author":[{"given":"Jun","family":"Shen","sequence":"first","affiliation":[{"name":"Fujian Provincial Cervical Disease Diagnosis and Treatment Health Center, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics Fujian Medical University  Fuzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Nursing Fujian Medical University  Fuzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pathology, Affiliated Hospital of Nantong University Nantong University  NanTong Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Xing","sequence":"additional","affiliation":[{"name":"The Alpert Medical School of Brown University, Department of Pediatrics Women &amp; Infants Hospital of Rhode Island  Providence Rhode Island USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Lin","sequence":"additional","affiliation":[{"name":"Women's Health Care Department Fu'an Maternal and Child Health Hospital  Fu'an Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics Fujian Medical University  Fuzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pathology, Affiliated Hospital of Nantong University Nantong University  NanTong Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuehui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Ophthalmology Department, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics Fujian Medical University  Fuzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiancui","family":"Chen","sequence":"additional","affiliation":[{"name":"Fujian Provincial Cervical Disease Diagnosis and Treatment Health Center, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics Fujian Medical University  Fuzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21696"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-22-0674"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"KimJ KimH KimH et\u00a0al.A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges. arXiv2025.","DOI":"10.1007\/s10462-025-11223-9"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078641"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-023-02965-w"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000004705"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms26104961"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.17251"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2023.08.008"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpobgyn.2021.03.014"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.34838"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2024.102992"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djad202"},{"key":"e_1_2_10_16_1","unstructured":"deSanjos\u00e9S PerkinsRB CamposNG et\u00a0al.Design of the HPV\u2010automated visual evaluation (PAVE) study: validating a novel cervical screening strategy.medRxiv: The Preprint Server for Health Sciences 2023: 2023.08.30.23294826."},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015720"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-51880-4"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2922682"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djy225"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113625"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2025.1522550"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1181982"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.k499"}],"container-title":["International Journal of Gynecology &amp; 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