{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:54:38Z","timestamp":1781585678272,"version":"3.54.5"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study sought to develop an innovative body composition (BC)\u2010based deep learning (DL) model to precisely evaluate survival in gastric cancer (GC) patients undergoing neoadjuvant treatment (NT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This retrospective study included GC patients undergoing NT from two centers. CT images both pre\u2010NT and post\u2010NT were preprocessed, focusing on the automatic segmentation of subcutaneous fat, visceral fat, and skeletal muscle regions using TotalSegmentator. Delta Radiomics features were extracted using Pyradiomics. After feature fusion and selection, the optimal model is Naive Bayes (Rad model). A hybrid DL model was developed by combining ResNet18 and Transformer networks for feature extraction. The Clinic_Rad_DL model was constructed by combining clinical features, radiomic signatures, and DL signatures. The ExtraTree classifier was used for the Clinic_Rad_DL model, while a separate Cox regression model was developed for survival analysis using the same features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 356 patients (mean age, 59\u2009\u00b1\u200910\u2009years; 264 males [74.2%]) were enrolled and divided into training, validation, and test sets in a 7:2:1 ratio. The DL model outperformed the Rad model. The Clinic_Rad_DL model outperformed both Rad model and DL model, with AUC of 0.915, 0.890, and 0.890 in training, validation, and test sets, respectively. The Cox proportional hazards model showed C\u2010index of 0.806, 0.803, and 0.819, effectively stratifying patients into high\u2010 and low\u2010risk groups with significant survival differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The study developed and validated a BC\u2010based DL model to predict survival in GC patients undergoing NT, offering potential for personalized treatment strategies in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71886","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:40:36Z","timestamp":1777704036000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition\u2010Based Deep Learning"],"prefix":"10.1002","volume":"15","author":[{"given":"Yingjing","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Gastric Surgery Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology, Shanghai Medical College Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Oncology, Shanghai Medical College Fudan University  Shanghai China"},{"name":"Department of Radiology Fudan University Shanghai Cancer Center  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Radiology Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology  Wuhan Hubei Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Su","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Cancer Center, Division of Gastrointestinal and Pancreatic Surgery Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College  Zhejiang Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianchun","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of General Surgery Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3948-4715","authenticated-orcid":false,"given":"Yakai","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Gastric Surgery Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology, Shanghai Medical College Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jncc.2024.01.006"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12516"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(21)00297-7"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.23.02167"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nut.2020.111135"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/jhn.12861"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/nu14235047"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-020-00887-2"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2017170056"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-020-00341-y"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcsm.13315"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1062937"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nut.2023.112336"},{"issue":"2","key":"e_1_2_11_17_1","article-title":"Introduction to Machine Learning, Neural Networks, and Deep Learning","volume":"9","author":"Choi R. 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