• Hao-Fan Wu, Jiang-Peng Yan, Qian Wu, Zhen Yu, Hong-Xia Xu, Chun-Hua Song, Zeng-Qing Guo, Wei Li, Yan-Jun Xiang, Zhe Xu, Jie Luo, Shu-Qun Cheng, Feng-Min Zhang, Han-Ping Shi, Cheng-Le Zhuang, and Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) Group.
• Colorectal Cancer Center/Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
• Nutrition. 2024 Mar 1; 119: 112317112317.

ObjectivesCancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort.MethodsThis was a nationwide multicenter observational study conducted from October 2012 to April 2021 in China. Unsupervised consensus clustering analysis was applied based on demographic, anthropometric, nutritional, oncological, and quality-of-life data. Key characteristics of each cluster were identified using the standardized mean difference. We used logistic and Cox regression analysis to evaluate 1-, 3-, 5-y, and overall mortality.ResultsA consensus clustering algorithm was performed for 4329 patients with cancer cachexia in the discovery cohort, and four clusters with distinct phenotypes were uncovered. From clusters 1 to 4, the clinical characteristics of patients showed a transition from almost unimpaired to mildly, moderately, and severely impaired. Consistently, an increase in mortality from clusters 1 to 4 was observed. The overall mortality rate was 32%, 40%, 54%, and 68%, and the median overall survival time was 21.9, 18, 16.7, and 13.6 mo for patients in clusters 1 to 4, respectively. Our machine learning-based model performed better in predicting mortality than the traditional model. External validation confirmed the above results.ConclusionsMachine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.Copyright © 2023 Elsevier Inc. All rights reserved.

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