{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:04:10Z","timestamp":1789434250716,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"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":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Sarcopenia is a common condition in patients with gastric cancer that is associated with poor survival and adverse clinical outcomes. Conventional diagnostic approaches are time\u2010consuming, limiting large\u2010scale clinical implementation. This study aimed to develop and validate a multimodal model that integrates clinical variables, radiomics features and deep learning features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1067 patients with gastric cancer from two medical centres were included. The diagnosis of sarcopenia required low grip strength and a low skeletal muscle index, whereas severe sarcopenia additionally required low gait speed. Radiomics features were extracted from computed tomography images at the third lumbar vertebral level to construct radiomics models. The 2D and 2.5D deep learning models were developed using the ResNet50 architecture. A transformer\u2010based multimodal model integrating clinical variables, radiomics features and 2.5D deep learning features was developed and termed the sarcopenia model, while an XGBoost fusion model integrating the same three modalities was constructed as a comparator. Model performance was evaluated in the training, validation and external test sets using receiver operating characteristic and decision curve analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the 1067 patients, 782 (73.3%) were male and 285 (26.7%) were female, with no significant difference in gender distribution across the training, validation and external test sets (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.34). Kaplan\u2013Meier survival analysis demonstrated that the overall survival of patients with sarcopenia was significantly worse than that of patients without sarcopenia (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). Among single\u2010modality models, the radiomics model achieved AUCs of 0.85 and 0.80 in the validation and external test sets, respectively. The 2.5D deep learning model outperformed the 2D model, yielding AUCs of 0.81 and 0.83 in the validation and external test sets. The XGBoost model achieved AUCs of 0.85 and 0.86 in the validation and external test sets, respectively. In the sarcopenia model, the AUCs for identifying sarcopenia were 0.95, 0.87 and 0.89 in the training, validation and external test sets, respectively, while those for identifying severe sarcopenia were 0.93, 0.85 and 0.84.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The sarcopenia model integrating clinical variables, radiomics features and deep learning features enables the accurate identification of sarcopenia and severe sarcopenia in patients with gastric cancer and may support clinical screening and risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.70377","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:47:01Z","timestamp":1789433221000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated Assessment of Sarcopenia in Patients with Gastric Cancer Using Deep Learning and Radiomics"],"prefix":"10.1002","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1438-118X","authenticated-orcid":false,"given":"Huaiqing","family":"Zhi","sequence":"first","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2205-6422","authenticated-orcid":false,"given":"Jingwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2888-6351","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ang","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8053-1335","authenticated-orcid":false,"given":"Dongze","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5233-264X","authenticated-orcid":false,"given":"Linxi","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinnuo","family":"He","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province 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Laboratory of Intelligent Cancer Biomarker Discovery and Translation The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"},{"name":"NHC Key Laboratory of Clinical Nutrition and Intervention (The First Affiliated Hospital of Wenzhou Medical University)  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1410-9982","authenticated-orcid":false,"given":"Weiteng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of General Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"229","article-title":"Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 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