{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T12:20:11Z","timestamp":1787746811116,"version":"build-2784847793"},"reference-count":40,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by\/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"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2009600"],"award-info":[{"award-number":["2022YFC2009600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cancer cachexia is a heterogeneous syndrome that remains frequently underrecognized in routine oncology care, especially before overt wasting develops. We aimed to develop and externally validate an explainable two\u2010stage machine learning framework for early cachexia identification and prognostic stratification using routinely available clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this multicentre real\u2010world study based on the INSCOC registry, 10\u2009796 hospitalized adults with cancer were analysed, including 3995 patients with cachexia and 6801 without cachexia. First\u2010stage machine learning models were trained separately in high\u2010 and low\u2010prevalence settings to identify cachexia. Second\u2010stage survival models were developed in 1711 cachexia patients with follow\u2010up data from a 5172\u2010patient follow\u2010up cohort. A total of 586 deaths were observed in the cachexia follow\u2010up cohort. External validation used an independent Fujian cohort of 886 patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Cachexia prevalence was 37.0% (3995\/10\u2009796). Compared with non\u2010cachexia patients, cachexia patients had lower median BMI (21.0 vs. 23.4\u2009kg\/m2), albumin (37.9 vs. 39.7\u2009g\/L), triceps skinfold thickness (13.0 vs. 16.0\u2009mm), calf circumference (33.0 vs. 35.0\u2009cm) and higher CRP (5.9 vs. 3.9\u2009mg\/L); all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001. For cachexia identification, XGBoost achieved the best discrimination, with an AUC of 0.867 (95% CI: 0.857\u20130.878), sensitivity 0.706 and specificity 0.846 in the high\u2010prevalence group, and an AUC of 0.870 (95% CI: 0.861\u20130.880), sensitivity 0.817 and specificity 0.756 in the low\u2010prevalence group. In external validation, the AUCs were 0.744 in the high\u2010prevalence subgroup and 0.694 in the low\u2010prevalence subgroup. Among cachexia patients with follow\u2010up, the 1\u2010year mortality rate was 20.8% (95% CI: 19.0%\u201322.9%). For prognostic stratification, the random survival forest model showed the most favourable overall performance, with a C\u2010index of 0.680 and time\u2010dependent AUCs of 0.800, 0.777, 0.741 and 0.716 at 3, 6, 9 and 12\u2009months, respectively. Advanced TNM stage and higher CRP were associated with worse survival, whereas higher albumin, prealbumin and HDL were associated with better survival.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This explainable two\u2010stage framework enables accurate cachexia identification across different prevalence settings and clinically meaningful prognostic stratification in patients with established cachexia. Using routine variables and independent external validation, it provides a practical basis for earlier recognition, risk\u2010adapted supportive care and precision management in oncology practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.70370","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T04:52:17Z","timestamp":1787374337000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early Identification and Prognostic Stratification of Cancer Cachexia Using Explainable Machine Learning: A Multicentre Cohort Study"],"prefix":"10.1002","volume":"17","author":[{"given":"Wei","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Clinical Nutrition and Department of Colorectal Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang China"},{"name":"The First School of Medicine and School of Information and Engineering Wenzhou Medical University  Wenzhou Zhejiang China"},{"name":"Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation The First Affiliated Hospital, Wenzhou Medical University  Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kan","family":"Pan","sequence":"additional","affiliation":[{"name":"Institute of Clinical Nutrition and Department of Colorectal Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou Zhejiang China"},{"name":"The First School of Medicine and School of Information and Engineering Wenzhou Medical University  Wenzhou Zhejiang China"},{"name":"Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation The First Affiliated Hospital, Wenzhou Medical University  Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery\/Department of Clinical Nutrition, Beijing Shijitan Hospital Capital Medical University  Beijing China"},{"name":"Key Laboratory of Cancer Foods for Special Medical Purpose (FSMP) for State Market Regulation  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery\/Department of Clinical Nutrition, Beijing Shijitan Hospital Capital Medical University  Beijing China"},{"name":"Key Laboratory of Cancer Foods for Special Medical Purpose (FSMP) for State Market Regulation  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuo","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal and Gland Surgery The First Affiliated Hospital, Guangxi Medical University  Nanning Guangxi 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