{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:38:59Z","timestamp":1788961139015,"version":"build-2803163510"},"reference-count":23,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2025\/08148\u20104"],"award-info":[{"award-number":["2025\/08148\u20104"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2017\/06579\u20101"],"award-info":[{"award-number":["2017\/06579\u20101"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["443992\/2023\u20101"],"award-info":[{"award-number":["443992\/2023\u20101"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["406947\/2023\u20106"],"award-info":[{"award-number":["406947\/2023\u20106"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Oral Pathology Medicine"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Oral leukoplakia (OL) is a potentially malignant disorder of the oral mucosa. Accurate prediction of malignant transformation (MT) remains a clinical challenge. This study aimed to develop and evaluate a machine learning model that integrates histopathological, demographic, and DNA content features to predict MT risk in OL.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study of 97 OL cases\u201418 with confirmed MT and 79 non\u2010transformed controls\u2014selected from a larger series. Each case included clinicopathological features, and DNA content data obtained by flow cytometry for cell cycle phases (G1, S\u2010phase, G2 and excess DNA beyond the tetraploid region [4cER]). All cases had a minimum 5\u2010year follow\u2010up or histologically confirmed transformation. A multilayer perceptron (MLP) model was trained on 27 features. Stratified five\u2010fold cross\u2010validation and minority class oversampling (positive filling) were used to improve learning and mitigate data imbalance. Performance was evaluated using accuracy, sensitivity, specificity, F1\u2010score, AUC, and Kaplan\u2013Meier survival analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Significant predictors of MT included 4cER (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.005), G2 phase (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.04), dysplasia grading (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003), and inflammatory infiltrate (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.01). The optimized model yielded 72% sensitivity, 96% specificity, and an AUC of 85.4%. Survival analysis showed significantly poorer outcomes in the high\u2010risk cases predicted by the model (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Integrating DNA content analysis with machine learning provides an objective and clinically useful model to stratify malignant risk in OL, complementing conventional histopathology and supporting personalized patient management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jop.70084","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T05:21:26Z","timestamp":1762924886000},"page":"249-260","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting Malignant Transformation in Oral Leukoplakia: A Multilayer Perceptron Approach Incorporating Clinicopathological Features and\n                    <scp>DNA<\/scp>\n                    Content"],"prefix":"10.1111","volume":"55","author":[{"given":"Guilherme Iani","family":"Pontes","sequence":"first","affiliation":[{"name":"Institute of Science and Technology Federal University of S\u00e3o Paulo (ICT\u2010UNIFESP)  S\u00e3o Jos\u00e9 dos Campos S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Lu\u00edza Damaceno","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Head and Neck Surgery Department University of S\u00e3o Paulo Medical School  S\u00e3o Paulo S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andresa Borges","family":"Soares","sequence":"additional","affiliation":[{"name":"Department of Surgery, Stomatology, Pathology, and Radiology, Area of Pathology, Bauru School of Dentistry University of S\u00e3o Paulo (FOB 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Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Sperandio","sequence":"additional","affiliation":[{"name":"Faculdade S\u00e3o Leopoldo Mandic  Campinas S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6019-6653","authenticated-orcid":false,"given":"Matheus Cardoso","family":"Moraes","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology Federal University of S\u00e3o Paulo (ICT\u2010UNIFESP)  S\u00e3o Jos\u00e9 dos Campos S\u00e3o Paulo 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