Explainable deep learning model WAL-net for individualized assessment of potentially reversible malnutrition in patients with cancer: a multicenter cohort study.

Journal: The British journal of nutrition
Published Date: Jul 10, 2025

Abstract

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilize machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicenter cohort study including hospitalized oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modeled using a long short-term memory (LSTM) architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalizability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set=2977, test set=1277). There were 2783 men and 1471 women (median age=61 years). RM was identified in 754 (17.7%) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated with the progressive stages of cancer cachexia (r=-0.340, <0.001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favorable performance to predict RM in the test set (AUC=0.924, 95%CI=0.904-0.944) and an external validation set (n=798, AUC=0.909, 95%CI=0.876-0.943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all <0.05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimize patient outcomes in multidisciplinary cancer care.

Authors

  • Liangyu Yin
    Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China; Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
  • Ning Tong
    School of Software, Dalian Jiaotong University, Dalian 116028, China.
  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Jiuwei Cui
    Cancer Center, The First Bethune Hospital of Jilin University, Changchun, Jilin, China.
  • Zengqing Guo
    Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, 350014, China. Electronic address: gzq_005@126.com.
  • Qinghua Yao
    Department of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital and Key Laboratory of Traditional Chinese Medicine Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
  • Fuxiang Zhou
    Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Clinical Cancer Study Center, Zhongnan Hospital, Wuhan University, Wuhan 430071, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.
  • Zhikang Chen
    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China. Electronic address: 2211100190@nbu.edu.cn.
  • Huiqing Yu
    Department of Palliative Care and Department of Geriatric Oncology, Chongqing University Cancer Hospital, Chongqing, China.
  • Tao Li
    Department of Emergency Medicine, Jining No.1 People's Hospital, Jining, China.
  • Zengning Li
    Department of Clinical Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050031, China.
  • Pingping Jia
    Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
  • Chunhua Song
    Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China.
  • Hongxia Xu
    Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China. Electronic address: hx_xu2015@163.com.
  • Hanping Shi
    Department of Gastrointestinal Surgery, Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. Electronic address: shihp@ccmu.edu.cn.

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