ORIGINAL RESEARCH article
Front. Nutr.
Sec. Clinical Nutrition
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1606470
Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients
Provisionally accepted
- 1Fujian Provincial Cancer Hospital, Fuzhou, China
- 2First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China
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Postoperative malnutrition is a prevalent complication following esophageal cancer surgery, significantly impairing clinical recovery and long-term prognosis. This study aimed to develop and validate predictive models using machine learning algorithms and a nomogram to estimate the risk of malnutrition at one month after esophagectomy. Methods: A total of 1,693 patients who underwent curative esophageal cancer surgery were analyzed, with 1,251 patients allocated to the development cohort and 442 to the validation cohort. Feature selection was performed via the least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning models were constructed and evaluated, alongside a nomogram developed through multivariable logistic regression. Results: The incidence of postoperative malnutrition was 45.4% (568/1,251) in the development cohort and 50.7% (224/442) in the validation cohort. Among machine learning models, the Random Forest (RF) model demonstrated optimal performance, achieving area under the receiver operating characteristic curve (AUC) values of 0.820 (95% CI: 0.796-0.845) and 0.805 (95% CI: 0.771-0.839) in the development and validation cohorts, respectively. The nomogram incorporated five clinically interpretable predictors: female gender, advanced age, low preoperative body mass index (BMI), neoadjuvant therapy history, and preoperative sarcopenia. It showed comparable discriminative ability, with AUCs of 0.801 (95% CI: 0.775-0.826) and 0.795 (95% CI: 0.764-0.828) in the respective cohorts (p > 0.05 vs. RF). Calibration curves revealed strong agreement between predicted and observed outcomes, while decision curve analysis (DCA) confirmed substantial clinical utility across risk thresholds. Conclusions: Both machine learning and the nomogram provide accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients. While RF showed marginally higher predictive performance, the nomogram offers superior clinical interpretability, making it a practical option for individualized risk stratification.
Keywords: esophageal cancer, Postoperative malnutrition, machine learning, nomogram, Surgery
Received: 05 Apr 2025; Accepted: 30 May 2025.
Copyright: © 2025 Lin, He, Chen, Ke, Chen and Mingfang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhenmeng Lin, Fujian Provincial Cancer Hospital, Fuzhou, China
Yan Mingfang, Fujian Provincial Cancer Hospital, Fuzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.