{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:52:34Z","timestamp":1787237554333,"version":"build-2736575974"},"reference-count":54,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand\u2010crafted and deep radiomics features extracted from B\u2010mode ultrasound images in differentiating benign and malignant thyroid nodules compared to senior and junior radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1602 thyroid nodules from four centers across two countries (Iran and Malaysia) were included for the development and validation of AI models. From each original and expanded contour, which included the peritumoral region, 2060 handcrafted and 1024 deep radiomics features were extracted to assess the effectiveness of the peritumoral region in the AI diagnosis profile. The performance of four algorithms, namely, support vector machine with linear (SVM_lin) and radial basis function (SVM_RBF) kernels, logistic regression, and K\u2010nearest neighbor, was evaluated. The diagnostic performance of the proposed AI model was compared with two radiologists based on the American Thyroid Association (ATA) and the Thyroid Imaging Reporting &amp; Data System (TI\u2010RADS\u2122) guidelines to show the model's applicability in clinical routines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty\u2010five hand\u2010crafted and 36 deep radiomics features were considered for model development. In the training step, SVM_RBF and SVM_lin showed the best results when rectangular contours 40% greater than the original contours were used for both hand\u2010crafted and deep features. Ensemble\u2010learning with SVM_RBF and SVM_lin obtained AUC of 0.954, 0.949, 0.932, and 0.921 in internal and external validations of the Iran cohort and Malaysia cohorts 1 and 2, respectively, and outperformed both radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed AI model trained on nodule+the peripheral region performed optimally in external validations and outperformed the radiologists using the ATA and TI\u2010RADS guidelines.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jum.16665","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T06:33:36Z","timestamp":1739601216000},"page":"1059-1074","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-0973","authenticated-orcid":false,"given":"Ali","family":"Abbasian Ardakani","sequence":"first","affiliation":[{"name":"Department of Radiology Technology School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afshin","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine Urmia University of Medical Science  Urmia Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chai Hong","family":"Yeong","sequence":"additional","affiliation":[{"name":"School of Medicine and Digital Health and Medical Advancement Impact Lab, Faculty of Health and Medical Sciences Taylor's University  Subang Jaya Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei Lin","family":"Ng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, Universiti Malaya, Jalan Universiti  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aik Hao","family":"Ng","sequence":"additional","affiliation":[{"name":"Clinical Oncology Unit, Faculty of Medicine University of Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kasturi Nair","family":"Tangaraju","sequence":"additional","affiliation":[{"name":"Department of Radiology National Cancer Institute  Putrajaya Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selda","family":"Behestani","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine Urmia University of Medical Science  Urmia Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Mirza\u2010Aghazadeh\u2010Attari","sequence":"additional","affiliation":[{"name":"Division of Vascular and Interventional Radiology, Russell H. 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