{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:58:42Z","timestamp":1790305122027,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Head &amp;amp; Neck"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Oral Cancer often occurs from the transformation of precursor lesions, and this offers an opportunity for early detection. Current methods to assess risk of precursor lesion progression to oral cancer incompletely predict risk. A multimodal framework that leverages machine learning is needed to improve prediction. In this manuscript, we propose more standardization in the study of OPMD across sites and propose a framework aligned to the principles of the NCI's Human Tissue Atlas Network (HTAN). We explore whole exome sequencing for genomic risk, methylation array for environmental genetic regulation, RNA sequencing for transcriptional control of cellular programs with spatial transcriptomics to fulfill the spatial requirements of HTAN. Moreover, we propose a machine learning (ML) analysis whereby a learned image\u2010molecular association is created by integrating H&amp;E histology and sequencing results. Once these associations are learned, a \u201cTeacher\u2010Student\u201d model can be created whereby the Student model can be deployed to infer the \u201cmolecular associations\u201d from H&amp;E and patient covariates alone. Such a framework integrating multiomic data across multiple different biologic processes and time points can help to better understand and potentially even predict the biologic events that underlie oral cancer initiation.<\/jats:p>","DOI":"10.1002\/hed.70465","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:07:27Z","timestamp":1790302047000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Oral Cancer From Precursor Lesions: The Case for a Standardized Framework of Analysis to Improve Prediction Modeling"],"prefix":"10.1002","author":[{"given":"Michael E.","family":"Troka","sequence":"first","affiliation":[{"name":"Division of Oral and Maxillofacial Surgery Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7491-8995","authenticated-orcid":false,"given":"James C.","family":"Gates","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2019.104550"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.32871"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/hed.26006"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/odi.13993"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djz238"},{"issue":"4","key":"e_1_2_7_7_1","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1258\/0022215021910744","article-title":"Waiting Times During the Management of Head and Neck Tumours","volume":"116","author":"Jones T. 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