The American journal of clinical nutrition · 2025
Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study.
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Yin Liangyu, Li Na, Lin Xin, Zhang Ling, Fan Yang, Liu Jie, Lu Zongliang, Li Wei, Cui Jiuwei, Guo Zengqing, Yao Qinghua, Zhou Fuxiang, Liu Ming, Chen Zhikang, Yu Huiqing, Li Tao, Li Zengning, Jia Pingping, Song Chunhua, Shi Hanping, Xu Hongxia
Аннотация
BACKGROUND: Cachexia is associated with multiple adverse outcomes in cancer. However, clinical decision-making for oncology patients at the cachexia stage presents significant challenges.
OBJECTIVES: This study aims to develop a machine learning (ML) model to identify potentially reversible cancer cachexia (PRCC).
METHODS: This was a multicenter cohort study. Cachexia was retrospectively diagnosed using Fearon's framework. PRCC was defined as a diagnosis of cancer cachexia at baseline that turned negative 1 mo later. Body weight dynamics accessible upon patient admission were screened and modeled to predict PRCC. Multiple ML models were trained and cross-validated using 70% of the data to predict PRCC, with the remaining 30% reserved for model evaluation. The interpretability and clinical usefulness of the optimal model were assessed, and external validation was performed in an independent cohort of 238 patients.
RESULTS: The study enrolled 1983 men and 1784 women (median age = 58 y). PRCC was identified in 1983 patients (52.6%). Breast cancer exhibited the highest rate of PRCC (72.1%), whereas cachexia associated with various gastrointestinal cancers was less likely to be reversed. Weight change (WC) from 6 mo ago to 1 mo ago, WC from 1 mo ago to baseline (-1 to 0), and baseline body mass index were selected for modeling. A multilayer perceptron model showed good performance to predict PRCC in the holdout test set [area under the curve (95% confidence interval): 0.887 (0.866, 0.907); accuracy: 0.836; sensitivity: 0.859; specificity: 0.812] and the external validation set [area under the curve (95% confidence interval): 0.863 (0.778, 0.948)]. The WC -1 to 0 showed the highest impact on model output. The model was demonstrated to be clinically useful and statistically relevant.
CONCLUSIONS: This study presents an explainable ML model for the early identification of PRCC that utilizes simple body weight dynamics. The findings showcase the potential of this approach in improving the management of cancer cachexia to optimize patient outcomes.
OBJECTIVES: This study aims to develop a machine learning (ML) model to identify potentially reversible cancer cachexia (PRCC).
METHODS: This was a multicenter cohort study. Cachexia was retrospectively diagnosed using Fearon's framework. PRCC was defined as a diagnosis of cancer cachexia at baseline that turned negative 1 mo later. Body weight dynamics accessible upon patient admission were screened and modeled to predict PRCC. Multiple ML models were trained and cross-validated using 70% of the data to predict PRCC, with the remaining 30% reserved for model evaluation. The interpretability and clinical usefulness of the optimal model were assessed, and external validation was performed in an independent cohort of 238 patients.
RESULTS: The study enrolled 1983 men and 1784 women (median age = 58 y). PRCC was identified in 1983 patients (52.6%). Breast cancer exhibited the highest rate of PRCC (72.1%), whereas cachexia associated with various gastrointestinal cancers was less likely to be reversed. Weight change (WC) from 6 mo ago to 1 mo ago, WC from 1 mo ago to baseline (-1 to 0), and baseline body mass index were selected for modeling. A multilayer perceptron model showed good performance to predict PRCC in the holdout test set [area under the curve (95% confidence interval): 0.887 (0.866, 0.907); accuracy: 0.836; sensitivity: 0.859; specificity: 0.812] and the external validation set [area under the curve (95% confidence interval): 0.863 (0.778, 0.948)]. The WC -1 to 0 showed the highest impact on model output. The model was demonstrated to be clinically useful and statistically relevant.
CONCLUSIONS: This study presents an explainable ML model for the early identification of PRCC that utilizes simple body weight dynamics. The findings showcase the potential of this approach in improving the management of cancer cachexia to optimize patient outcomes.