{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:19:12Z","timestamp":1787663952077,"version":"build-2736575974"},"reference-count":32,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"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":["Cancer Medicine"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate preoperative pathological classification of malignant and borderline ovarian tumors (OMTs) can support surgical planning, fertility preservation, and prognosis, but ultrasound\u2010based subtype assessment remains difficult because imaging phenotypes are heterogeneous, class imbalance is common, and interpretation varies among operators. This study developed and evaluated an exploratory deep learning framework for multiclass ovarian tumor classification under small\u2010sample conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single\u2010center retrospective study used ultrasound images from patients with surgically and pathologically confirmed OMTs treated between August 2017 and October 2022. Six pathological types were included. After quality control, preprocessing, and augmentation, the dataset was split at the patient level into training, validation, and test sets at a 5:3:2 ratio. An optimized ConvNeXt model served as the baseline. STL\u2010DeepBDC combined stratified transfer learning with a Deep Bilinear Discriminative Covariance module. Performance was evaluated using accuracy, sensitivity, specificity, macro\u2010F1 score, AUC, AUPRC, and Grad\u2010CAM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>STL\u2010DeepBDC outperformed the optimized ConvNeXt baseline. Accuracy increased from 0.51 to 0.67, macro\u2010average sensitivity from 0.53 to 0.68, specificity from 0.87 to 0.92, and macro\u2010F1 score from 0.52 to 0.66. Macro\u2010average AUC improved from 0.75 to 0.87, and macro\u2010average AUPRC from 0.49 to 0.63. Category\u2010level AUCs ranged from 0.76 to 0.94 and AUPRCs from 0.51 to 0.78, suggesting more stable discrimination across six pathological types under class imbalance. Ablation analysis supported the contributions of STL and DeepBDC. Grad\u2010CAM highlighted clinically relevant regions, including thickened cyst walls, papillary projections, septations, and solid components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>STL\u2010DeepBDC provides an exploratory, interpretable framework for ultrasound\u2010based multiclass ovarian tumor classification in a limited\u2010sample setting. Larger multicenter studies with external validation are required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72165","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T02:05:56Z","timestamp":1787277956000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["STL\u2010DeepBDC: A Robust Few\u2010Shot Learning Framework for Multiclass Ovarian Tumor Classification in Ultrasound Outperforms Conventional Transfer Learning"],"prefix":"10.1002","volume":"15","author":[{"given":"Ning","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Ultrasound The Fourth Affiliated Hospital of Harbin Medical University  Harbin Heilongjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanxi","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Harbin 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