{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:30:19Z","timestamp":1791469819759,"version":"4.3.4"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006579","name":"Ministry of Industry and Information Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["\u7f16\u53f7 CEIEC-2020-ZM02-0103\/03"],"award-info":[{"award-number":["\u7f16\u53f7 CEIEC-2020-ZM02-0103\/03"]}],"id":[{"id":"10.13039\/501100006579","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019331","name":"Henan Medical Science and Technique Foundation","doi-asserted-by":"publisher","award":["\u7f16\u53f7LHGJ20190157"],"award-info":[{"award-number":["\u7f16\u53f7LHGJ20190157"]}],"id":[{"id":"10.13039\/501100019331","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019331","name":"Henan Medical Science and Technique Foundation","doi-asserted-by":"publisher","award":["\u7f16\u53f7SBGJ202103078"],"award-info":[{"award-number":["\u7f16\u53f7SBGJ202103078"]}],"id":[{"id":"10.13039\/501100019331","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong\u2019s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT <jats:italic>vs<\/jats:italic>. MPGT, AUC=0.863; PA <jats:italic>vs<\/jats:italic>. MPGT, AUC=0.929; WT <jats:italic>vs<\/jats:italic>. MPGT, AUC=0.825; PA <jats:italic>vs<\/jats:italic>. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT <jats:italic>vs<\/jats:italic>. MPGT, AUC=0.907; PA <jats:italic>vs<\/jats:italic>. MPGT, AUC=0.961; WT <jats:italic>vs<\/jats:italic>. MPGT, AUC=0.879; PA <jats:italic>vs<\/jats:italic>. WT, AUC=0.967).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2022.937050","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T04:56:53Z","timestamp":1657515413000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics 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