{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T12:54:19Z","timestamp":1782219259332,"version":"3.54.5"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["21624304"],"award-info":[{"award-number":["21624304"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82402262"],"award-info":[{"award-number":["82402262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82227802"],"award-info":[{"award-number":["82227802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Projects in Guangzhou","award":["2025A04J3480"],"award-info":[{"award-number":["2025A04J3480"]}]},{"name":"Science and Technology Projects in Guangzhou","award":["202201020022, 2023A03J1036, 2023A03J1038"],"award-info":[{"award-number":["202201020022, 2023A03J1036, 2023A03J1038"]}]},{"name":"Science and Technology Projects in Guangzhou","award":["2025A04J7006"],"award-info":[{"award-number":["2025A04J7006"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFF1204600"],"award-info":[{"award-number":["2023YFF1204600"]}]},{"name":"Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University","award":["No. JNU1AF-CFTP-2022-a01201"],"award-info":[{"award-number":["No. JNU1AF-CFTP-2022-a01201"]}]},{"name":"Outstanding Young Talents of Guangdong Special Support Program","award":["0720240213"],"award-info":[{"award-number":["0720240213"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02183-z","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:38:03Z","timestamp":1764783483000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A transformer-based prognostic signature integrating tumor and body composition CT images predicts postoperative recurrence in gastric cancer"],"prefix":"10.1038","volume":"9","author":[{"given":"Qiuying","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueyue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian","family":"Jian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenle","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuixing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"2183_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229\u2013263 (2024).","journal-title":"CA Cancer J. Clin."},{"key":"2183_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1093\/annonc\/mdy502","volume":"30","author":"K Muro","year":"2019","unstructured":"Muro, K. et al. Pan-Asian adapted ESMO Clinical Practice Guidelines for the management of patients with metastatic gastric cancer: a JSMO-ESMO initiative endorsed by CSCO, KSMO, MOS, SSO and TOS. Ann. Oncol. 30, 19\u201333 (2019).","journal-title":"Ann. Oncol."},{"key":"2183_CR3","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1093\/annonc\/mdz180","volume":"30","author":"G Pentheroudakis","year":"2019","unstructured":"Pentheroudakis, G. & Committee, E. G. Recent eUpdates to the ESMO Clinical Practice Guidelines on hepatocellular carcinoma, cancer of the pancreas, soft tissue and visceral sarcomas, cancer of the prostate and gastric cancer. Ann. Oncol. 30, 1395\u20131397 (2019).","journal-title":"Ann. Oncol."},{"key":"2183_CR4","first-page":"93","volume":"67","author":"MB Amin","year":"2017","unstructured":"Amin, M. B. et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \u201cpersonalized\u201d approach to cancer staging. CA Cancer J. Clin. 67, 93\u201399 (2017).","journal-title":"CA Cancer J. Clin."},{"key":"2183_CR5","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/S0140-6736(11)61873-4","volume":"379","author":"YJ Bang","year":"2012","unstructured":"Bang, Y. J. et al. Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial. Lancet 379, 315\u2013321 (2012).","journal-title":"Lancet"},{"key":"2183_CR6","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1016\/j.clnu.2020.11.007","volume":"40","author":"QT Dong","year":"2021","unstructured":"Dong, Q. T. et al. Influence of body composition, muscle strength, and physical performance on the postoperative complications and survival after radical gastrectomy for gastric cancer: A comprehensive analysis from a large-scale prospective study. Clin. Nutr. 40, 3360\u20133369 (2021).","journal-title":"Clin. Nutr."},{"key":"2183_CR7","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.clnu.2016.07.015","volume":"36","author":"J Arends","year":"2017","unstructured":"Arends, J. et al. ESPEN guidelines on nutrition in cancer patients. Clin. Nutr. 36, 11\u201348 (2017).","journal-title":"Clin. Nutr."},{"key":"2183_CR8","doi-asserted-by":"publisher","unstructured":"Lim, H. S., Lee, B., Cho, I. & Cho, G. S. Nutritional and Clinical Factors Affecting Weight and Fat-Free Mass Loss after Gastrectomy in Patients with Gastric Cancer. Nutrients 12, https:\/\/doi.org\/10.3390\/nu12071905 (2020).","DOI":"10.3390\/nu12071905"},{"key":"2183_CR9","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1016\/j.jand.2012.03.012","volume":"112","author":"JV White","year":"2012","unstructured":"White, J. V. et al. Consensus statement of the Academy of Nutrition and Dietetics\/American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). J. Acad. Nutr. Diet. 112, 730\u2013738 (2012).","journal-title":"J. Acad. Nutr. Diet."},{"key":"2183_CR10","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1148\/radiol.2020201640","volume":"298","author":"K Magudia","year":"2021","unstructured":"Magudia, K. et al. Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves. Radiology 298, 319\u2013329 (2021).","journal-title":"Radiology"},{"key":"2183_CR11","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1002\/mp.15437","volume":"49","author":"C Li","year":"2022","unstructured":"Li, C. et al. Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer. Med. Phys. 49, 1535\u20131546 (2022).","journal-title":"Med. Phys."},{"key":"2183_CR12","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1002\/bjs.11928","volume":"108","author":"C Jin","year":"2021","unstructured":"Jin, C. et al. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br. J. Surg. 108, 542\u2013549 (2021).","journal-title":"Br. J. Surg."},{"key":"2183_CR13","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1200\/JCO.2012.45.2722","volume":"31","author":"L Martin","year":"2013","unstructured":"Martin, L. et al. Cancer Cachexia in the Age of Obesity: Skeletal Muscle Depletion Is a Powerful Prognostic Factor, Independent of Body Mass Index. J. Clin. Oncol. 31, 1539\u20131547 (2013).","journal-title":"J. Clin. Oncol."},{"key":"2183_CR14","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/S1470-2045(08)70153-0","volume":"9","author":"CM Prado","year":"2008","unstructured":"Prado, C. M. et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 9, 629\u2013635 (2008).","journal-title":"Lancet Oncol."},{"key":"2183_CR15","doi-asserted-by":"publisher","unstructured":"Miao, S. et al. Deep learning radiomics under multimodality explore association between muscle\/fat and metastasis and survival in breast cancer patients. Brief Bioinform. 23, https:\/\/doi.org\/10.1093\/bib\/bbac432 (2022).","DOI":"10.1093\/bib\/bbac432"},{"key":"2183_CR16","doi-asserted-by":"publisher","first-page":"e2032269","DOI":"10.1001\/jamanetworkopen.2020.32269","volume":"4","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y. et al. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw. Open 4, e2032269 (2021).","journal-title":"JAMA Netw. Open"},{"key":"2183_CR17","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1007\/s10120-022-01328-3","volume":"25","author":"J Zhang","year":"2022","unstructured":"Zhang, J. et al. Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study. Gastric Cancer 25, 1050\u20131059 (2022).","journal-title":"Gastric Cancer"},{"key":"2183_CR18","unstructured":"Vaswani, A. et al. Attention is all you need. In 31st Conference on Neural Information Processing Systems (NIPS 2017)."},{"key":"2183_CR19","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1049\/ipr2.12419","volume":"16","author":"RS Wang","year":"2022","unstructured":"Wang, R. S. et al. Medical image segmentation using deep learning: A survey. IET Image Process 16, 1243\u20131267 (2022).","journal-title":"IET Image Process"},{"key":"2183_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. A ConvNet for the 2020s. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 11966-11976 (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"2183_CR21","doi-asserted-by":"publisher","unstructured":"Chen, J. Y. et al. TransMorph: Transformer for unsupervised medical image registration. Med. Image Anal. 82, https:\/\/doi.org\/10.1016\/j.media.2022.102615 (2022).","DOI":"10.1016\/j.media.2022.102615"},{"key":"2183_CR22","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A. et al. UNETR: Transformers for 3D Medical Image Segmentation. In IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 1748\u20131758 (IEEE, 2022).","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"2183_CR23","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-87231-1_6","volume":"12906","author":"ZC Zhang","year":"2021","unstructured":"Zhang, Z. C., Yu, L. Q., Liang, X. K., Zhao, W. & Xing, L. TransCT: Dual-Path Transformer for Low Dose Computed Tomography. Lect. Notes Comput Sc. 12906, 55\u201364 (2021).","journal-title":"Lect. Notes Comput Sc."},{"key":"2183_CR24","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1139\/H08-075","volume":"33","author":"M Mourtzakis","year":"2008","unstructured":"Mourtzakis, M. et al. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl. Physiol. Nutr. Metab. 33, 997\u20131006 (2008).","journal-title":"Appl. Physiol. Nutr. Metab."},{"key":"2183_CR25","doi-asserted-by":"publisher","first-page":"86","DOI":"10.21037\/apm.2018.08.02","volume":"8","author":"MP Davis","year":"2019","unstructured":"Davis, M. P. & Panikkar, R. Sarcopenia associated with chemotherapy and targeted agents for cancer therapy. Ann. Palliat. Med. 8, 86\u2013101 (2019).","journal-title":"Ann. Palliat. Med."},{"key":"2183_CR26","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1007\/s00432-016-2230-4","volume":"142","author":"DD Huang","year":"2016","unstructured":"Huang, D. D. et al. Sarcopenia predicts 1-year mortality in elderly patients undergoing curative gastrectomy for gastric cancer: a prospective study. J. Cancer Res. Clin. Oncol. 142, 2347\u20132356 (2016).","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"2183_CR27","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1245\/s10434-018-6452-3","volume":"25","author":"T Kawamura","year":"2018","unstructured":"Kawamura, T. et al. Long-Term Outcomes of Gastric Cancer Patients with Preoperative Sarcopenia. Ann. Surg. Oncol. 25, 1625\u20131632 (2018).","journal-title":"Ann. Surg. Oncol."},{"key":"2183_CR28","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1245\/s10434-017-5811-9","volume":"24","author":"K Kudou","year":"2017","unstructured":"Kudou, K. et al. Prognostic Significance of Sarcopenia in Patients with Esophagogastric Junction Cancer or Upper Gastric Cancer. Ann. Surg. Oncol. 24, 1804\u20131810 (2017).","journal-title":"Ann. Surg. Oncol."},{"key":"2183_CR29","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1245\/s10434-017-6269-5","volume":"25","author":"J Lu","year":"2018","unstructured":"Lu, J. et al. A Novel Preoperative Skeletal Muscle Measure as a Predictor of Postoperative Complications, Long-Term Survival and Tumor Recurrence for Patients with Gastric Cancer After Radical Gastrectomy. Ann. Surg. Oncol. 25, 439\u2013448 (2018).","journal-title":"Ann. Surg. Oncol."},{"key":"2183_CR30","doi-asserted-by":"publisher","unstructured":"Zhuang, C. L. et al. Sarcopenia is an Independent Predictor of Severe Postoperative Complications and Long-Term Survival After Radical Gastrectomy for Gastric Cancer Analysis from a Large-Scale Cohort. Medicine 95, https:\/\/doi.org\/10.1097\/MD.0000000000003164 (2016).","DOI":"10.1097\/MD.0000000000003164"},{"key":"2183_CR31","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1159\/000526787","volume":"78","author":"Q Lan","year":"2022","unstructured":"Lan, Q. et al. Radiomics in Addition to Computed Tomography-Based Body Composition Nomogram May Improve the Prediction of Postoperative Complications in Gastric Cancer Patients. Ann. Nutr. Metab. 78, 316\u2013327 (2022).","journal-title":"Ann. Nutr. Metab."},{"key":"2183_CR32","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1097\/RLU.0000000000004253","volume":"47","author":"R Anconina","year":"2022","unstructured":"Anconina, R. et al. Combined 18 F-FDG PET\/CT Radiomics and Sarcopenia Score in Predicting Relapse-Free Survival and Overall Survival in Patients With Esophagogastric Cancer. Clin. Nucl. Med. 47, 684\u2013691 (2022).","journal-title":"Clin. Nucl. Med."},{"key":"2183_CR33","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1007\/s10120-025-01614-w","volume":"28","author":"Q Chen","year":"2025","unstructured":"Chen, Q. et al. Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection. Gastric Cancer 28, 684\u2013695 (2025).","journal-title":"Gastric Cancer"},{"key":"2183_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102762","volume":"85","author":"J Li","year":"2023","unstructured":"Li, J. et al. Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives. Med. Image Anal. 85, 102762 (2023).","journal-title":"Med. Image Anal."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02183-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02183-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02183-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T04:39:48Z","timestamp":1767760788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02183-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2183"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02183-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,3]]},"assertion":[{"value":"15 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12"}}