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

Published online by Cambridge University Press:  10 July 2025

Liangyu Yin*
Affiliation:
Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing400037, People’s Republic of China
Ning Tong
Affiliation:
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing400042, People’s Republic of China
Na Li
Affiliation:
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing400042, People’s Republic of China
Jie Liu
Affiliation:
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing400042, People’s Republic of China
Wei Li
Affiliation:
Cancer Center of the First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of China
Jiuwei Cui
Affiliation:
Cancer Center of the First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of China
Zengqing Guo
Affiliation:
Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian350014, People’s Republic of China
Qinghua Yao
Affiliation:
Department of Integrated Chinese and Western Medicine, Cancer Hospital of the University of Chinese Academy of Science (Zhejiang Cancer Hospital), Hangzhou, Zhejiang310022, People’s Republic of China
Fuxiang Zhou
Affiliation:
Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei430071, People’s Republic of China
Ming Liu
Affiliation:
Department of Colorectal Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang150001, People’s Republic of China
Zhikang Chen
Affiliation:
Department of Colorectal and Anal Surgery, Xiangya Hospital of Central South University, Changsha, Hunan410008, People’s Republic of China
Huiqing Yu
Affiliation:
Department of Palliative Care and Department of Geriatric Oncology, Chongqing University Cancer Hospital, Chongqing400030, People’s Republic of China
Tao Li
Affiliation:
Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan610041, People’s Republic of China
Zengning Li
Affiliation:
Department of Clinical Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei050031, People’s Republic of China
Pingping Jia
Affiliation:
Department of Gastrointestinal Surgery and Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing100038, People’s Republic of China
Chunhua Song
Affiliation:
Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan450001, People’s Republic of China
Hongxia Xu*
Affiliation:
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing400042, People’s Republic of China
Hanping Shi*
Affiliation:
Department of Gastrointestinal Surgery and Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing100038, People’s Republic of China Key Laboratory of Cancer FSMP for State Market Regulation, Beijing100038, People’s Republic of China Lead Contact and Principal Investigator of the Investigation on Nutrition Status and Its Clinical Outcome of Common Cancers (INSCOC) Project, Beijing, People’s Republic of China
*
Corresponding authors: Liangyu Yin, Emails: liangyuyin1988@tmmu.edu.cn, liangyuyin1988@qq.com; Hongxia Xu, Email: hx_xu2015@163.com; Hanping Shi, Email: shihp@ccmu.edu.cn
Corresponding authors: Liangyu Yin, Emails: liangyuyin1988@tmmu.edu.cn, liangyuyin1988@qq.com; Hongxia Xu, Email: hx_xu2015@163.com; Hanping Shi, Email: shihp@ccmu.edu.cn
Corresponding authors: Liangyu Yin, Emails: liangyuyin1988@tmmu.edu.cn, liangyuyin1988@qq.com; Hongxia Xu, Email: hx_xu2015@163.com; Hanping Shi, Email: shihp@ccmu.edu.cn

Abstract

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised 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 modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability 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 to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable 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 P < 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 optimise patient outcomes in multidisciplinary cancer care.

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Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Nutrition Society

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References

Cederholm, T, Barazzoni, R, Austin, P, et al. (2017) ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr 36, 4964.CrossRefGoogle ScholarPubMed
Wang, Z, Barazzoni, R & Shi, H (2023) Medical nutrition is one basic human right. Precision Nutr 2, e00026.Google Scholar
Arends, J, Bachmann, P, Baracos, V, et al. (2017) ESPEN guidelines on nutrition in cancer patients. Clin Nutr 36, 1148.CrossRefGoogle ScholarPubMed
Yin, L, Song, C, Cui, J, et al. (2021) A fusion decision system to identify and grade malnutrition in cancer patients: machine learning reveals feasible workflow from representative real-world data. Clin Nutr 40, 49584970.CrossRefGoogle ScholarPubMed
Yin, L, Lin, X, Liu, J, et al. (2021) Classification tree-based machine learning to visualize and validate a decision tool for identifying malnutrition in cancer patients. JPEN J Parenteral Enteral Nutr 45, 17361748.CrossRefGoogle ScholarPubMed
Gioulbasanis, I, Baracos, VE, Giannousi, Z, et al. (2011) Baseline nutritional evaluation in metastatic lung cancer patients: mini nutritional assessment v. weight loss history. Ann Oncol: Offic J Eur Soc Med Oncol 22, 835841.CrossRefGoogle Scholar
Almasaudi, AS, McSorley, ST, Dolan, RD, et al. (2019) The relation between Malnutrition Universal Screening Tool (MUST), computed tomography-derived body composition, systemic inflammation, and clinical outcomes in patients undergoing surgery for colorectal cancer. Am J Clin Nutr 110, 13271334.CrossRefGoogle ScholarPubMed
Hu, C, Barazzoni, R & Shi, H (2023) Nutritional care is the first-line therapy for many conditions. Precision Nutr 2, e00059.CrossRefGoogle Scholar
Rosato, E, Gigante, A, Colalillo, A, et al. (2023) GLIM-diagnosed malnutrition predicts mortality and risk of hospitalization in systemic sclerosis: a retrospective study. Eur J Intern Med 117, 103110.CrossRefGoogle ScholarPubMed
Brown, D, Loeliger, J, Stewart, J, et al. (2023) Relationship between global leadership initiative on malnutrition (GLIM) defined malnutrition and survival, length of stay and post-operative complications in people with cancer: a systematic review. Clin Nutr 42, 255268.CrossRefGoogle ScholarPubMed
Arends, J, Baracos, V, Bertz, H, et al. (2017) ESPEN expert group recommendations for action against cancer-related malnutrition. Clin Nutr 36, 11871196.CrossRefGoogle Scholar
Gyan, E, Raynard, B, Durand, JP, et al. (2018) Malnutrition in patients with cancer: comparison of perceptions by patients, relatives, and physicians-results of the NutriCancer2012 Study. JPEN J Parenteral Enteral Nutr 42, 255260.CrossRefGoogle ScholarPubMed
Attar, A, Malka, D, Sabate, JM, et al. (2012) Malnutrition is high and underestimated during chemotherapy in gastrointestinal cancer: an AGEO prospective cross-sectional multicenter study. Nutr Cancer 64, 535542.CrossRefGoogle ScholarPubMed
Hebuterne, X, Lemarie, E, Michallet, M, et al. (2014) Prevalence of malnutrition and current use of nutrition support in patients with cancer. JPEN J Parenteral Enteral Nutr 38, 196204.CrossRefGoogle ScholarPubMed
Kipouros, M, Vamvakari, K, Kalafati, IP, et al. (2023) The level of adherence to the ESPEN guidelines for energy and protein intake prospectively influences weight loss and nutritional status in patients with cancer. Nutrients 15, 4232.CrossRefGoogle Scholar
Cederholm, T, Jensen, GL, Correia, M, et al. (2019) GLIM criteria for the diagnosis of malnutrition - a consensus report from the global clinical nutrition community. Clin Nutr 38, 19.CrossRefGoogle ScholarPubMed
Murnane, LC, Forsyth, AK, Koukounaras, J, et al. (2023) Malnutrition defined by GLIM criteria identifies a higher incidence of malnutrition and is associated with pulmonary complications after oesophagogastric cancer surgery, compared to ICD-10-defined malnutrition. J Surg Oncol 128, 769780.CrossRefGoogle ScholarPubMed
Matsui, R, Rifu, K, Watanabe, J, et al. (2023) Impact of malnutrition as defined by the GLIM criteria on treatment outcomes in patients with cancer: a systematic review and meta-analysis. Clin Nutr 42, 615624.CrossRefGoogle Scholar
Kiss, N, Steer, B, de van der Schueren, M, et al. (2023) Machine learning models to predict outcomes at 30-days using Global Leadership Initiative on Malnutrition combinations with and without muscle mass in people with cancer. J Cachexia, Sarcopenia Muscle 14, 18151823.CrossRefGoogle ScholarPubMed
Yin, L, Chong, F, Huo, Z, et al. (2022) GLIM-defined malnutrition and overall survival in cancer patients: a meta-analysis. JPEN J Parenteral Enteral Nutr 47, 207219.CrossRefGoogle ScholarPubMed
Yin, L, Lin, X, Li, N, et al. (2021) Evaluation of the global leadership initiative on malnutrition criteria using different muscle mass indices for diagnosing malnutrition and predicting survival in lung cancer patients. JPEN J Parenteral Enteral Nutr 45, 607617.CrossRefGoogle ScholarPubMed
Milanez, DSJ, Razzera, EL, Lima, J, et al. (2023) Feasibility and criterion validity of the GLIM criteria in the critically ill: a prospective cohort study. JPEN J Parenteral Enteral Nutr 47, 754765.CrossRefGoogle ScholarPubMed
Yin, L, Fan, Y, Lin, X, et al. (2021) Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer. Br J Nutr 111.Google ScholarPubMed
Yin, L, Cheng, N, Chen, P, et al. (2021) Association of malnutrition, as defined by the PG-SGA, ESPEN 2015, and GLIM criteria, with complications in esophageal cancer patients After Esophagectomy. Front Nutr 8, 632546.CrossRefGoogle ScholarPubMed
Peng, D, Zong, K, Yang, H, et al. (2022) Malnutrition diagnosed by the Global Leadership Initiative on Malnutrition criteria predicting survival and clinical outcomes of patients with cancer: a systematic review and meta-analysis. Front Nutr 9, 1053165.CrossRefGoogle Scholar
Liu, C, Lu, Z, Li, Z, et al. (2021) Influence of malnutrition according to the GLIM criteria on the clinical outcomes of hospitalized patients with cancer. Front Nutr 8, 774636.CrossRefGoogle Scholar
Barazzoni, R, Jensen, GL, Correia, M, et al. (2022) Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition (GLIM) diagnosis of malnutrition. Clin Nutr 41, 14251433.CrossRefGoogle ScholarPubMed
O’Donoghue, N, Shrotriya, S, Aktas, A, et al. (2019) Clinical significance of weight changes at diagnosis in solid tumours. Support Care Cancer: Offic J Multinational Assoc Support Care Cancer 27, 27252733.Google ScholarPubMed
Hsu, WH, Ko, AT, Weng, CS, et al. (2023) Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer. J Cachexia, Sarcopenia Muscle 14, 20442053.CrossRefGoogle ScholarPubMed
Xu, H, Song, C, Yin, L, et al. (2022) Extension protocol for the Investigation on Nutrition Status and Clinical Outcome of Patients with Common Cancers in China (INSCOC) study: 2021 update. Precision Nutr 1, e00014.Google Scholar
Kondrup, J, Allison, SP, Elia, M, et al. (2003) ESPEN guidelines for nutrition screening 2002. Clin Nutr 22, 415421.CrossRefGoogle ScholarPubMed
Ottery, FD (1994) Rethinking nutritional support of the cancer patient: the new field of nutritional oncology. Semin Oncol 21, 770778.Google ScholarPubMed
Chen, C, Lu, FC & Department of Disease Control Ministry of Health PRC (2004) The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci: BES 17, 136.Google ScholarPubMed
Fearon, K, Strasser, F, Anker, SD, et al. (2011) Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12, 489495.CrossRefGoogle ScholarPubMed
Chen, LK, Woo, J, Assantachai, P, et al. (2020) Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Directors Assoc 21, 300307.e2.CrossRefGoogle Scholar
Yin, L, Li, N, Lin, X, et al. (2025) Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study. Am J Clin Nutr 121, 535547.CrossRefGoogle ScholarPubMed
Yin, L, Zhang, L, Li, L, et al. (2024) Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: a multicentre cohort study. J Cachexia, Sarcopenia Muscle 15, 11771186.CrossRefGoogle ScholarPubMed
Yin, L, Liu, J, Liu, M, et al. (2023) Ensemble learning system to identify nutritional risk and malnutrition in cancer patients without weight loss information. Sci China Life Sci 66, 12001203.CrossRefGoogle ScholarPubMed
Yin, L, Cui, J, Lin, X, et al. (2022) Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge. Am J Clin Nutr 116, 12291239.CrossRefGoogle ScholarPubMed
Bouloubasi, Z, Karayiannis, D, Pafili, Z, et al. (2023) Re-assessing the role of peri-operative nutritional therapy in patients with pancreatic cancer undergoing surgery: a narrative review. Nutr Res Rev 110.Google ScholarPubMed
Yaceczko, S & Baltz, J (2023) Evaluation of nutrition components within prehabilitation programs in gastrointestinal cancers: is prehab worth the hype? Nutr Clin Pract: Offic Publ Am Soc Parenteral Enteral Nutr 39, 117128.CrossRefGoogle ScholarPubMed
Zhou, T, Yang, K, Thapa, S, et al. (2017) Differences in symptom burden among cancer patients with different stages of cachexia. J Pain Symptom Manage 53, 919926.CrossRefGoogle ScholarPubMed
Nakajima, N (2021) Differential diagnosis of cachexia and refractory cachexia and the impact of appropriate nutritional intervention for cachexia on survival in terminal cancer patients. Nutrients 13, 915.CrossRefGoogle ScholarPubMed
McLuskie, A, Bowers, M, Bayly, J, et al. (2025) Nutritional interventions in randomised clinical trials for people with incurable solid cancer: a systematic review. Clin Nutr 44, 201219.CrossRefGoogle ScholarPubMed
Baguley, BJ, Edbrooke, L, Denehy, L, et al. (2024) A rapid review of nutrition and exercise approaches to managing unintentional weight loss, muscle loss, and malnutrition in cancer. The Oncol (Epublication ahead of print version 07 October 2024).Google Scholar
Muscaritoli, M, Imbimbo, G, Jager-Wittenaar, H, et al. (2023) Disease-related malnutrition with inflammation and cachexia. Clin Nutr 42, 14751479.CrossRefGoogle ScholarPubMed
Penet, MF & Bhujwalla, ZM (2015) Cancer cachexia, recent advances, and future directions. Cancer J 21, 117122.CrossRefGoogle ScholarPubMed
Roeland, EJ, Bohlke, K, Baracos, VE, et al. (2020) Management of cancer cachexia: ASCO guideline. J Clin Oncol: Offic J Am Soc Clin Oncol 38, 24382453.CrossRefGoogle ScholarPubMed
Evans, WJ, Morley, JE, Argiles, J, et al. (2008) Cachexia: a new definition. Clin Nutr 27, 793799.CrossRefGoogle ScholarPubMed
Argiles, JM, Lopez-Soriano, FJ, Toledo, M, et al. (2011) The cachexia score (CASCO): a new tool for staging cachectic cancer patients. J Cachexia, Sarcopenia Muscle 2, 8793.CrossRefGoogle Scholar
Schuetz, P, Fehr, R, Baechli, V, et al. (2019) Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial. Lancet 393, 23122321.CrossRefGoogle Scholar
Wen, X, Wang, M, Jiang, CM, et al. (2011) Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults. Asia Pac J Clin Nutr 20, 551556.Google ScholarPubMed
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