{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:06:34Z","timestamp":1784001994349,"version":"3.55.0"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["No.23ZR1438000"],"award-info":[{"award-number":["No.23ZR1438000"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Oral Diseases"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a multimodal dual\u2010step support vector machine model (SVM\u2010DualNet) for the preoperative three\u2010class classification of salivary gland lesions (SGLs) to support clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively collected clinical, conventional ultrasound (CUS), shear wave elastography (SWE), and radiomics features from 284 patients with SGLs. For malignancy discrimination and pleomorphic adenoma identification, linear SVM models based on different modality combinations were constructed and compared. The best\u2010performing binary models were sequentially combined to form SVM\u2010DualNet. SHapley Additive exPlanations (SHAP) were applied for global and case\u2010level interpretation and incorporated into diagnostic assistance. Clinical utility was evaluated by comparing the junior radiologist's diagnostic performance before and after SHAP assistance and by comparison with the senior radiologist.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the test cohort, SVM\u2010DualNet achieved a balanced accuracy of 0.76 and a macro F1 score of 0.82 for the three\u2010class classification. The binary models discriminated malignancy and pleomorphic adenoma with AUCs of 0.90 (95% CI: 0.82\u20130.97) and 0.85 (95% CI: 0.76\u20130.94), respectively. SHAP\u2010assisted review improved the junior radiologist's balanced accuracy from 0.55 to 0.70 and macro F1 from 0.57 to 0.75, approaching the senior radiologist's performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The model provides reliable preoperative classification of SGLs and can assist clinicians in decision\u2010making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/odi.70199","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:20:16Z","timestamp":1773314416000},"page":"1315-1329","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Multimodal Data to Clinical Insight: An Explainable Model for Preoperative Salivary Gland Lesion Diagnosis"],"prefix":"10.1111","volume":"32","author":[{"given":"Jing","family":"Feng","sequence":"first","affiliation":[{"name":"Department of Ultrasound, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqi","family":"Yan","sequence":"additional","affiliation":[{"name":"Computer Science Clark University  Worcester Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai Liang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6052-8830","authenticated-orcid":false,"given":"Ang Ang","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"He","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8755-6815","authenticated-orcid":false,"given":"Ping","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai 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