{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T04:19:31Z","timestamp":1780114771963,"version":"3.54.0"},"reference-count":30,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004885","name":"Ume\u00e5 Universitet","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004885","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":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Interpretable machine learning (ML) for early detection of cancer has the potential to improve risk assessment and early intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 261 proteins related to inflammation and\/or tumor processes in 123 blood samples collected from healthy persons, but of whom a sub\u2010group later developed squamous cell carcinoma of the oral tongue (SCCOT), were analyzed. Samples from people who developed SCCOT within less than 5\u2009years were classified as tumor\u2010to\u2010be and all other samples as tumor\u2010free. The optimal ML algorithm for feature selection was identified and feature importance computed by the SHapley Additive exPlanations (SHAP) method. Five popular ML algorithms (AdaBoost, Artificial neural networks [ANNs], Decision Tree [DT], eXtreme Gradient Boosting [XGBoost], and Support Vector Machine [SVM]) were applied to establish prediction models, and decisions of the optimal models were interpreted by SHAP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using the 22 selected features, the SVM prediction model showed the best performance (sensitivity\u2009=\u20090.867, specificity\u2009=\u20090.859, balanced accuracy\u2009=\u20090.863, area under the receiver operating characteristic curve [ROC\u2010AUC]\u2009=\u20090.924). SHAP analysis revealed that the 22 features rendered varying person\u2010specific impacts on model decision and the top three contributors to prediction were Interleukin 10 (IL10), TNF Receptor Associated Factor 2 (TRAF2), and Kallikrein Related Peptidase 12 (KLK12).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using multidimensional plasma protein analysis and interpretable ML, we outline a systematic approach for early detection of SCCOT before the appearance of clinical signs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jop.13461","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T11:05:28Z","timestamp":1688987128000},"page":"637-643","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Early detection of squamous cell carcinoma of the oral tongue using multidimensional plasma protein analysis and interpretable machine learning"],"prefix":"10.1111","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6574-3628","authenticated-orcid":false,"given":"Xiaolian","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Medical Biosciences\/Pathology Ume\u00e5 University  Ume\u00e5 V\u00e4sterbotten Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2166-6242","authenticated-orcid":false,"given":"Amir","family":"Salehi","sequence":"additional","affiliation":[{"name":"Department of Medical Biosciences\/Pathology Ume\u00e5 University  Ume\u00e5 V\u00e4sterbotten Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Biosciences\/Pathology Ume\u00e5 University  Ume\u00e5 V\u00e4sterbotten 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