{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:18:22Z","timestamp":1780499902294,"version":"3.54.1"},"reference-count":58,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Head &amp;amp; Neck"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate pretreatment assessment of the extent of tumor invasion and status of cervical lymph node metastasis is essential for staging and treatment planning in HNSCC. Deep learning (DL) shows promise but is limited by methodological heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic review and network meta\u2010analysis (PRISMA). Studies (2019\u20132025) evaluating DL models for pretreatment lymph node or tumor invasion classification using CT, MRI, PET\/CT, or SPECT\/CT were included. Diagnostic performance (AUC) was pooled using random\u2010effects models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Twenty\u2010three studies were included. Pooled AUC for lymph node classification was 0.78 (95% CI 0.72\u20130.84). In subgroup analyses, performance was lower in multicenter and externally validated studies (AUC 0.84) than in single\u2010center studies (AUC 0.92;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.029), and slightly lower with radiologic versus pathologic ground truth (0.83 vs. 0.87;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.093). Network meta\u2010analysis showed a nonsignificant advantage of fusion models. Pooled AUC for tumor invasion was 0.84. DL models outperformed human readers (\u0394AUC +0.09).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>DL demonstrates strong diagnostic performance for pretherapeutic HNSCC imaging, but results depend on study design. Standardized datasets and prospective multicenter validation are required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hed.70292","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:08:27Z","timestamp":1777446507000},"page":"1971-1983","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning for Pretreatment Imaging\u2010Based Tumor and Nodal Classification in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Network Meta\u2010Analysis"],"prefix":"10.1002","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5520-4635","authenticated-orcid":false,"given":"Jannik","family":"Ketschau","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery TUM University Hospital Klinikum Rechts der Isar, School of Medicine and Health, Technical University of Munich  Munich 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