个人中心| | 帮助
高级检索
当前位置： 首页 > 详情页

Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study

| 认领 | 导出 | |

文献详情

资源类型：
Pubmed体系：
作者：
Yin Liangyu[1,2] * ; Li Na[1] ; Lin Xin[1] ; Zhang Ling[1] ; Fan Yang[1] ; Liu Jie[1] ; Lu Zongliang[1] ; Li Wei[3] ; Cui Jiuwei[3] ; Guo Zengqing[4] ; Yao Qinghua[5] ; Zhou Fuxiang[6] ; Liu Ming[7] ; Chen Zhikang[8] ; Yu Huiqing[9] ; Li Tao[10] ; Li Zengning[11] ; Jia Pingping[12] ; Song Chunhua[13] ; Shi Hanping[12,14] ; Xu Hongxia[1] ;
机构： [1]Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease,ChongqingClinical Research Center of Kidney and Urology Diseases,Xinqiao Hospital,Army Medical University (Third Military Medical University), Chongqing 400037,China [2]Department of Clinical Nutrition, Daping Hospital, Army Medical University(Third Military Medical University), Chongqing 400042, China [3]Cancer Center of the First Hospital of Jilin University,Changchun,Jilin 130021, China [4]Department of Medical Oncology,Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian 350014,China [5]Department of Integrated Chineseand WestemMedicine,Cancer Hospital of the University of Chinese Academy of Science (Zhejiang Cancer Hospital),Hangzhou, Zhejiang 310022,China [6]Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan,Hubei 430071,China [7]Department of Colorectal Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China [8]Department of Colorectal and Anal Surgery, Xiangya Hospital of Central South University, Changsha, Hunan 410008, China [9]Department of Palliative Care and Department of Geriatric Oncology,Chongqing University Cancer Hospital, Chongqing400030, China [10]Department of Radiation Oncology,Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu,Sichuan 610041, China [11]Department of Clinical Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei 050031, China [12]Department of Gastrointestinal Surgery and Department of Clinical Nutrition,Beijing Shijitan Hospital, Capital Medical University, Beijing 100038,China [13]Department of Epidemiology,College of Public Health,Zhengzhou University, Zhengzhou, Henan 450001, China [14]Key Laboratory of Cancer FSMP for State Market Regulation, Beijing 100038,China
出处：
DOI： 10.1016/j.ajcnut.2025.01.006
ISSN：

关键词： Cancer cachexia Decision Machine learning Body weight

摘要：
Cachexia is associated with multiple adverse outcomes in cancer. However, clinical decision-making for oncology patients at the cachexia stage presents significant challenges.This study aims to develop a machine learning (ML) model to identify potentially reversible cancer cachexia (PRCC).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.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.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.Copyright © 2025 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.

基金：
This work was supported by the National Natural Science Foundation of China (82304131), Natural Science Foundation of Chongqing, China(2024NSCQ-MSX3470 and CSTB2022NSCQ-MSX1069)and the National Key Research and Development Program of China(2017YFC1309200).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2025]版：
无
最新[2023]版：
大类 | 1 区 医学
小类 | 1 区 营养学
第一作者：
第一作者机构： [1]Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease,ChongqingClinical Research Center of Kidney and Urology Diseases,Xinqiao Hospital,Army Medical University (Third Military Medical University), Chongqing 400037,China [2]Department of Clinical Nutrition, Daping Hospital, Army Medical University(Third Military Medical University), Chongqing 400042, China
通讯作者：
通讯机构： [1]Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease,ChongqingClinical Research Center of Kidney and Urology Diseases,Xinqiao Hospital,Army Medical University (Third Military Medical University), Chongqing 400037,China [2]Department of Clinical Nutrition, Daping Hospital, Army Medical University(Third Military Medical University), Chongqing 400042, China [12]Department of Gastrointestinal Surgery and Department of Clinical Nutrition,Beijing Shijitan Hospital, Capital Medical University, Beijing 100038,China [14]Key Laboratory of Cancer FSMP for State Market Regulation, Beijing 100038,China
推荐引用方式(GB/T 7714)：
Yin Liangyu,Li Na,Lin Xin,et al.Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study[J].The American Journal Of Clinical Nutrition.2025,doi:10.1016/j.ajcnut.2025.01.006.
APA：
Yin Liangyu,Li Na,Lin Xin,Zhang Ling,Fan Yang...&Xu Hongxia.(2025).Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study.The American Journal Of Clinical Nutrition,,
MLA：
Yin Liangyu,et al."Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study".The American Journal Of Clinical Nutrition .(2025)

相关文献

[1]Optimization of Diagnosis-Related Groups for 14,246 Patients with Uterine Leiomyoma in a Single Center in Western China Using a Machine Learning Model [2]Classification Tree-Based Machine Learning to Visualize and Validate a Decision Tool for Identifying Malnutrition in Cancer Patients. [3]Optimization of anesthetic decision-making in ERAS using Bayesian network [4]Accurate breast cancer diagnosis using a stable feature ranking algorithm [5]Impact of remaining kidney volume to body weight ratio on renal function in living kidney donors [6]Machine learning for predicting preoperative red blood cell demand [7]Precise prediction of phase-separation key residues by machine learning [8]Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning. [9]Multimodal Prevention Strategies for Cancer Cachexia [10]机器学习在癌症患者自我报告结局中的应用研究进展

资源点击量：54684 今日访问量：0 总访问量：4646 更新日期：2025-03-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持：重庆聚合科技有限公司 地址：成都市人民南路四段55号