{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:50:20Z","timestamp":1781002220529,"version":"3.54.1"},"reference-count":37,"publisher":"SAGE Publications","issue":"3-4","license":[{"start":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T00:00:00Z","timestamp":1743206400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"the Foundation of the Chongqing Yongchuan District Technology Innovation and Application Development Project","award":["(No.2024yc-cxfz30047"],"award-info":[{"award-number":["(No.2024yc-cxfz30047"]}]},{"DOI":"10.13039\/501100004374","name":"Chongqing Medical University","doi-asserted-by":"crossref","award":["(No. YJJL2024066)"],"award-info":[{"award-number":["(No. YJJL2024066)"]}],"id":[{"id":"10.13039\/501100004374","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>\n                    Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was conducted on 91 patients who underwent ultrasonography examinations between January 2016 and December 2023 and were subsequently diagnosed with pleomorphic adenoma or Warthin\u2019s tumor based on postoperative pathological findings. A total of 526 ultrasonography images were collected for analysis. Convolutional neural network (CNN) models, including ResNet18, MobileNetV3Small, and InceptionV3, were trained and validated using these images for the differentiation of pleomorphic adenoma and Warthin\u2019s tumor. Performance evaluation metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were utilized. Two ultrasound physicians, with varying levels of expertise, conducted independent evaluations of the ultrasound images. Subsequently, a comparative analysis was performed between the diagnostic outcomes of the ultrasound physicians and the results obtained from the best-performing model. Inter-rater agreement between routine ultrasonography interpretation by the two expert ultrasonographers and the automatic identification diagnosis of the best model in relation to pathological results was assessed using kappa tests. The deep learning models achieved favorable performance in differentiating pleomorphic adenoma from Warthin\u2019s tumor. The ResNet18, MobileNetV3Small, and InceptionV3 models exhibited diagnostic accuracies of 82.4% (AUC: 0.932), 87.0% (AUC: 0.946), and 77.8% (AUC: 0.811), respectively. Among these models, MobileNetV3Small demonstrated the highest performance. The experienced ultrasonographer achieved a diagnostic accuracy of 73.5%, with sensitivity, specificity, positive predictive value, and negative predictive value of 73.7%, 73.3%, 77.8%, and 68.8%, respectively. The less-experienced ultrasonographer achieved a diagnostic accuracy of 69.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 66.7%, 71.4%, 71.4%, and 66.7%, respectively. The kappa test revealed strong consistency between the best-performing deep learning model and postoperative pathological diagnoses (kappa value: .778,\n                    <jats:italic>p<\/jats:italic>\n                    -value\u2009&lt;\u2009.001). In contrast, the less-experienced ultrasonographer demonstrated poor consistency in image interpretations (kappa value: .380,\n                    <jats:italic>p<\/jats:italic>\n                    -value\u2009&lt;\u2009.05). The diagnostic accuracy of the best deep learning model was significantly higher than that of the ultrasonographers, and the experienced ultrasonographer exhibited higher diagnostic accuracy than the less-experienced one. This study demonstrates the promising performance of a deep learning-based method utilizing ultrasonography images for the differentiation of pleomorphic adenoma and Warthin\u2019s tumor. The approach reduces subjective errors, provides decision support for clinicians, and improves diagnostic consistency.\n                  <\/jats:p>","DOI":"10.1177\/01617346251319410","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T17:35:26Z","timestamp":1743269726000},"page":"107-114","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors"],"prefix":"10.1177","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1921-9251","authenticated-orcid":false,"given":"Yajuan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Ultrasound, Yongchuan Hospital of Chongqing Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingchi","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Yongchuan Hospital of Chongqing Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaogang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Yongchuan Hospital of Chongqing Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Yongchuan Hospital of Chongqing Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Yongchuan Hospital of Chongqing Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfeng","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Yongchuan Hospital of Chongqing Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2020.08.002"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1159\/000528090"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11247360"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.5152\/dir.2019.18337"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/hed.2890080309"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcms.2014.01.014"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(19860915)58:6<1305::AID-CNCR2820580621>3.0.CO;2-K"},{"key":"e_1_3_2_9_2","article-title":"\u2018Metastasizing pleomorphic salivary adenoma\u2019 should now be considered a low-grade malignancy with a lethal potential","volume":"13","author":"Bradley PJ.","year":"2005","unstructured":"Bradley PJ. \u2018Metastasizing pleomorphic salivary adenoma\u2019 should now be considered a low-grade malignancy with a lethal potential. Curr Opin Otolaryngol Head Neck Surg. 2005;13:123-26.","journal-title":"Curr Opin Otolaryngol Head Neck Surg"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-020-06250-x"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.18004519314"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00428-018-2320-6"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11081467"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-017-4650-4"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1118351"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.669542"},{"issue":"1","key":"e_1_3_2_17_2","first-page":"19","article-title":"Cross-sectional imaging and cytologic investigations in the preoperative diagnosis of parotid gland tumors \u2013 an updated literature review","volume":"21","author":"Stoia S","year":"2021","unstructured":"Stoia S, B\u0103ciu\u021b G, Lenghel M, Badea R, Csutak C, Rusu GM, et al. Cross-sectional imaging and cytologic investigations in the preoperative diagnosis of parotid gland tumors \u2013 an updated literature review. Bosn J Basic Med Sci. 2021;21(1):19-32.","journal-title":"Bosn J Basic Med Sci"},{"issue":"11","key":"e_1_3_2_18_2","first-page":"1897","article-title":"Fine-needle aspiration cytology for parotid tumors","volume":"12","author":"Taniuchi M","year":"2022","unstructured":"Taniuchi M, Terada T, Kawata R. Fine-needle aspiration cytology for parotid tumors. Life (Basel). 2022;12(11):1897.","journal-title":"Life (Basel)"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.4132\/KoreanJPathol.2012.46.6.569"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25137350"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.02.018"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-022-02728-4"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13215546"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019190372"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.7150\/thno.52508"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.14309\/ctg.0000000000000393"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105344"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-022-02469-z"},{"key":"e_1_3_2_29_2","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Howard A","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan MX, et al Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019."},{"issue":"6","key":"e_1_3_2_30_2","first-page":"727","article-title":"Current developments in diagnosis of salivary gland tumors: from structure to artificial intelligence","volume":"14","author":"Faur AC","year":"2024","unstructured":"Faur AC, Buzas R, L\u0103z\u0103rescu AE, Ghenciu LA, et al Current developments in diagnosis of salivary gland tumors: from structure to artificial intelligence. Life (Basel). 2024;14(6):727.","journal-title":"Life (Basel)"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.21037\/qims-22-950"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21144758"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-14276-y"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.937050"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14235804"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-09968-2"},{"key":"e_1_3_2_37_2","first-page":"8192999","article-title":"The diagnostic value of ultrasound-based deep learning in differentiating parotid gland tumors","volume":"1","author":"Wang Y","year":"2022","unstructured":"Wang Y, Xie W, Huang S, Feng M, Ke X, Zhong Z, et al The diagnostic value of ultrasound-based deep learning in differentiating parotid gland tumors. J Oncol. 2022;1:8192999.","journal-title":"J Oncol"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"}],"container-title":["Ultrasonic Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346251319410","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01617346251319410","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346251319410","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:29:23Z","timestamp":1777652963000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01617346251319410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,29]]},"references-count":37,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1177\/01617346251319410"],"URL":"https:\/\/doi.org\/10.1177\/01617346251319410","relation":{},"ISSN":["0161-7346","1096-0910"],"issn-type":[{"value":"0161-7346","type":"print"},{"value":"1096-0910","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,29]]}}}