[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121212-en":3,"doc-seo-121212-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121212,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning","Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma remains challenging despite established criteria for follicular-pattern thyroid tumors and a recent World Health Organization reclassification. A supervised machine-learning approach was developed using proteomics from 46 thyroid tissue samples, followed by performance testing. A random forest classifier using five protein biomarkers distinguished the invasive encapsulated follicular variant from non-malignant samples with perfect training performance (AUC 1.00; accuracy 1.00). Protein-level receiver operating characteristic analyses supported differentiation across related groups, integrating high-throughput proteomics with machine learning.","pISSN 2383-7837 ∙ eISSN 2383-7845  \nORIGINAL ARTICLE  \nJournal of Pathology and Translational Medicine 2025; 59: 39-49 [https://doi.org/10.4132/jptm.2024.09.14](https://doi.org/10.4132/jptm.2024.09.14)  \nDiagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning  \nTruong Phan-Xuan Nguyen1, Minh-Khang Le2, Sittiruk Roytrakul3, Shanop Shuangshoti1,4, Nakarin Kitkumthorn5, Somboon Keelawat1,6  \nGraphical abstract  \n􀀑ĂĐŬŐƌŽƵŶĚ DĞƚŚŽĚƐ  \n• dŚLJƌŽŝĚ ĐĂŶĐĞƌ ŝƐ ƚŚĞ ŵŽƐƚ ĐŽŵŵŽŶĞŶĚŽĐƌŝŶĞ ŵĂůŝŐŶĂŶĐLJ͘  \n• ZĞĐĞŶƚ ĐůĂƐƐŝĨŝĐĂƚŝŽŶƐ ĞŵƉŚĂƐŝnjĞ ƚŚĞ ŶĞĞĚĨŽƌ ĂĐĐƵƌĂƚĞ ĚŝĨĨĞƌĞŶƚŝĂƚŝŽŶ ŽĨ ŝŶǀĂƐŝǀĞĞŶĐĂƉƐƵůĂƚĞĚ ĨŽůůŝĐƵůĂƌ ǀĂƌŝĂŶƚ ƉĂƉŝůůĂƌLJƚŚLJƌŽŝĚ ĐĂƌĐŝŶŽŵĂ ;/􀀜&sWd􀀒Ϳ ĨƌŽŵ ŽƚŚĞƌƚŚLJƌŽŝĚ ƚƵŵŽƌƐ͘  \ndŽ ĚĞǀĞůŽƉ Ă ŵĂĐŚŝŶĞͲůĞĂƌŶŝŶŐ ŵŽĚĞů ƵƐŝŶŐ ƉƌŽƚĞŽŵŝĐƐ ĚĂƚĂ ƚŽŝĚĞŶƚŝĨLJ /􀀜&sWd􀀒 ĂĐĐƵƌĂƚĞůLJ  \n• hƚŝůŝnjĞĚ ůŝƋƵŝĚ ĐŚƌŽŵĂƚŽŐƌĂƉŚLJ ͲƚĂŶĚĞŵ ŵĂƐƐ ƐƉĞĐƚƌŽŵĞƚƌLJ ;>􀀒 Ͳ D^ͬD^Ϳ ĨŽƌ ƉƌŽƚĞŽŵŝĐ ĂŶĂůLJƐŝƐ͘  \n•  \nZĞƐƵůƚƐ  \nZK􀀒 ĂŶĂůLJƐĞƐ  \n􀀄 dƌĂŝŶ ƐĂŵƉůĞƐ ;ŶсϯϲͿ  \nϭ͘ϬϬ  \nϬ͘ϳϱ  \nϬ͘ϱϬ  \nϬ͘Ϯϱ  \nϬ͘ϬϬϬ͘ϬϬ Ϭ͘Ϯϱ Ϭ͘ϱϬ Ϭ͘ϳϱ ϭ͘ϬϬ  \nϭ Ͳ ^ƉĞĐŝĨŝĐŝƚLJ  \n^ĞŶƐŝƚŝǀŝƚLJ  \n􀀑 dĞƐƚ ƐĂŵƉůĞƐ ;ŶсϭϬͿ  \nϬ͘ϬϬ Ϭ͘Ϯϱ Ϭ͘ϱϬ Ϭ͘ϳϱ ϭ͘ϬϬ  \nϭ Ͳ ^ƉĞĐŝĨŝĐŝƚLJ  \nCalibration ƉůŽƚƐ  \n􀀒 dƌĂŝŶ ƐĂŵƉůĞƐ ;ŶсϯϲͿ 􀀘 dĞƐƚ ƐĂŵƉůĞƐ ;ŶсϭϬͿ  \n􀀄ĐƚƵĂů WƌŽďĂďŝůŝƚLJ  \nϭ͘ϬϬ  \nϬ͘ϳϱ  \nϬ͘ϱϬ  \nϬ͘Ϯϱ  \nϬ͘ϬϬ  \n• KƵƌ ŵŽĚĞů ƐŚŽǁĞĚŚŝŐŚ ZK􀀒 􀀄h􀀒Ɛ сϭϬϬĂŶĚ ŐŽŽĚ ĐĂůŝďƌĂƚŝŽŶƐŝŶ ƚŚĞ ƚǁŽ ŐƌŽƵƉƐ;ƚŚĞ ƚƌĂŝŶŝŶŐ ĂŶĚŝŶƚĞƌŶĂů ƚĞƐƚͿ͘  \n􀀄 ƚŽƚĂů ŽĨ ϰϲ ƚŚLJƌŽŝĚ ƚŝƐƐƵĞƐĂŵƉůĞƐ ǁĞƌĞ ƐƚƵĚŝĞĚ͕ ŝŶĐůƵĚŝŶŐďŽƚŚ /􀀜&sWd􀀒 ĂŶĚ ŶŽŶͲ/􀀜&sWd􀀒ƐƉĞĐŝŵĞŶƐ͘  \n• 􀀄 ZĂŶĚŽŵ &ŽƌĞƐƚ ĐůĂƐƐŝĨŝĞƌ ƵƐŝŶŐĨŝǀĞ ŬĞLJ ƉƌŽƚĞŝŶ ďŝŽŵĂƌŬĞƌƐ ;􀁿􀀜􀀑ϭ͕ EhWϵϴ͕ 􀀒Ϯ􀀒Ϯ>͕ EW􀀄Wϭ͕ \u003C􀀒E:ϯͿ ǁĂƐĚĞǀĞůŽƉĞĚ ĨŽƌ ĚŝĂŐŶŽƐŝƐ͘  \nConfusion ŵĂƚƌŝĐĞƐ  \n􀀜  \nZĞĨĞƌĞŶĐĞ  \nŶŽŶ Ͳ / 􀀜 &sWd􀀒 /􀀜&sWd􀀒  \ndƌĂŝŶ ƐĂŵƉůĞƐ ;ŶсϯϲͿ &  \nϬ  \nϮϲ  \nϭϬ  \nϬ  \nZĞĨĞƌĞŶĐĞ  \nŶŽŶ Ͳ / 􀀜 &sWd􀀒 /􀀜&sWd􀀒  \nŶŽŶͲ/􀀜&sWd􀀒  \n/􀀜&sWd􀀒  \nWƌĞĚŝĐƚŝŽŶ  \ndĞƐƚ ƐĂŵƉůĞƐ ;ŶсϭϬͿ  \nϭ Ϯ  \n ϳ  Ϭ  \nŶŽŶͲ/􀀜&sWd􀀒 /􀀜&sWd􀀒  \nWƌĞĚŝĐƚŝŽŶ  \n• dŚĞ ŵŽĚĞů ĂĐĐƵƌĂƚĞůLJƉƌĞĚŝĐƚĞĚ Ăůů ƚŚĞƐĂŵƉůĞƐ ǁŝƚŚŽƵƚ ĂŶLJĞƌƌŽƌƐ ŝŶ ƚƌĂŝŶŝŶŐƐĂŵƉůĞƐ ĂŶĚ ĂůŵŽƐƚ ĂůůƚŚĞ ĐĂƐĞƐ ŝŶ ƚĞƐƚŝŶŐƐĂŵƉůĞƐ͘  \n􀀒KE􀀒>h^/KE^  \ndŚŝƐ ƐƚƵĚLJ ĚĞŵŽŶƐƚƌĂƚĞĚ ƚŚĂƚ ƚŚĞ ŝŶƚĞŐƌĂƚŝŽŶ ŽĨ ŚŝŐŚͲƚŚƌŽƵŐŚƉƵƚ ƉƌŽƚĞŽŵŝĐƐ ǁŝƚŚ ŵĂĐŚŝŶĞ ůĞĂƌŶŝŶŐ ĐĂŶ ĞĨĨĞĐƚŝǀĞůLJ ĚŝĨĨĞƌĞŶƚŝĂƚĞŝŶǀĂƐŝǀĞ ĞŶĐĂƉƐƵůĂƚĞĚ ĨŽůůŝĐƵůĂƌ ǀĂƌŝĂŶƚ ŽĨ ƉĂƉŝůůĂƌLJ ƚŚLJƌŽŝĚ ĐĂƌĐŝŶŽŵĂ ĨƌŽŵ ŽƚŚĞƌ ĨŽůůŝĐƵůĂƌ ƉĂƚƚĞƌŶ ƚŚLJƌŽŝĚ ƚƵŵŽƌƐ͘  \nEŐƵLJĞŶ d Ğƚ Ăů͘ :ŽƵƌŶĂů ŽĨ WĂƚŚŽůŽŐLJ ĂŶĚ dƌĂŶƐůĂƚŝŽŶĂů DĞĚŝĐŝŶĞ  \npISSN 2383-7837 ∙ eISSN 2383-7845  \nORIGINAL ARTICLE  \nJournal of Pathology and Translational Medicine 2025; 59: 39-49 [https://doi.org/10.4132/jptm.2024.09.14](https://doi.org/10.4132/jptm.2024.09.14)  \nDiagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning  \nTruong Phan-Xuan Nguyen1, Minh-Khang Le2, Sittiruk Roytrakul3, Shanop Shuangshoti1,4, Nakarin Kitkumthorn5, Somboon Keelawat1,6  \n1Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 2Department of Pathology, University of Yamanashi, Chuo City, Japan  \n3Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, Thailand  \n4Chulalongkorn GenePRO Center, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 5Department of Oral Biology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand  \n6Precision Pathology of Neoplasia Research Group, Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand  \nBackground: Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promis","cbCaisSInFVgTlyu","https://ap.wps.com/l/cbCaisSInFVgTlyu","pdf",2168414,1,12,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"Why is diagnosing invasive encapsulated follicular variant papillary thyroid carcinoma challenging?\",\"answer\":\"The follicular-pattern criteria are established, but distinguishing this invasive encapsulated variant from low-risk follicular pattern tumors is difficult due to shared morphological characteristics.\"},{\"question\":\"What machine-learning approach and biomarker set were used in the study?\",\"answer\":\"The study used a supervised model and a random forest classifier based on five protein biomarkers: ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3.\"},{\"question\":\"How well did the model perform in distinguishing the target carcinoma?\",\"answer\":\"In training samples, the classifier achieved AUCs of 1.00 and accuracy rates of 1.00 for separating the invasive encapsulated follicular variant from non-malignant samples.\"}]","Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning | 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is diagnosing invasive encapsulated follicular variant papillary thyroid carcinoma challenging?","Question",{"text":75,"@type":76},"The follicular-pattern criteria are established, but distinguishing this invasive encapsulated variant from low-risk follicular pattern tumors is difficult due to shared morphological characteristics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine-learning approach and biomarker set were used in the study?",{"text":80,"@type":76},"The study used a supervised model and a random forest classifier based on five protein biomarkers: ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3.",{"name":82,"@type":73,"acceptedAnswer":83},"How well did the model perform in distinguishing the target carcinoma?",{"text":84,"@type":76},"In training samples, the classifier achieved AUCs of 1.00 and accuracy rates of 1.00 for separating the invasive encapsulated follicular variant from non-malignant 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