{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T22:53:55Z","timestamp":1785365635960,"version":"3.55.0"},"reference-count":39,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010256","name":"Guangzhou Municipal Science and Technology Project","doi-asserted-by":"publisher","award":["2023A04J2240"],"award-info":[{"award-number":["2023A04J2240"]}],"id":[{"id":"10.13039\/501100010256","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gastric cancer is a biologically aggressive disease, accounting for a substantial proportion of cancer\u2010related deaths globally. Accurate localization of the lesion by artificial intelligence techniques helps timely and efficiently diagnose and treat. Segment Anything Model (SAM) has demonstrated considerable potential in medical image segmentation by displaying high performance in numerous image benchmark tests. However, its resource\u2010intensive nature limits feasibility in embedded medical contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study proposed GC\u2010SAM, a lightweight model for tumor segmentation. The architecture of GC\u2010SAM is innovatively proposed, including a knowledge distillation image encoder, prompt encoder, and mask decoder, which effectively replaces the conventional fixed and computationally intensive network components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Extensive experiments demonstrate that GC\u2010SAM significantly outperforms both classical segmentation models and recent state\u2010of\u2010the\u2010art networks. On the internal test set, GC\u2010SAM achieves 0.8186 Dice and 0.6504 mIoU, while reducing inference time and parameter count by over 80% compared to the original SAM. On the external dataset, GC\u2010SAM maintains superior performance (Dice 0.8350), demonstrating excellent generalization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed GC\u2010SAM model shows strong capability in segmenting gastric cancer tissue, while also demonstrating practical potential for deployment in embedded medical imaging devices.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71246","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T04:35:56Z","timestamp":1758688556000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Segment Anything Model for Gastric Cancer"],"prefix":"10.1002","volume":"14","author":[{"given":"Lanlan","family":"Li","sequence":"first","affiliation":[{"name":"College of Physics and Information Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongyang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyue","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of General Surgery (Colorectal Surgery) The Sixth Affiliated Hospital, Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongan","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Third People's Hospital, Fujian University of Traditional Chinese Medicine  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dabiao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Chemical and Engineering Fuzhou University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Decao","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Urology Guangdong Second Provincial General Hospital  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-3236","authenticated-orcid":false,"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Endoscopic Surgery The Sixth Affiliated Hospital, Sun Yat\u2010sen University  Guangzhou China"},{"name":"Department of General Surgery (Endoscopic Surgery) The Sixth Affiliated Hospital, Sun Yat\u2010sen University  Guangzhou China"},{"name":"The Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital, Sun Yat\u2010sen University  Guangzhou China"},{"name":"The Biomedical Innovation Center The Sixth Affiliated Hospital, Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.2217\/iim.09.9"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-019-0252-y"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30154-8"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.33384"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26216-9"},{"key":"e_1_2_11_7_1","first-page":"234","volume-title":"U\u2010Net: Convolutional Networks for Biomedical Image Segmentation. 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