Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients
BackgroundPostoperative 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...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Nutrition |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1606470/full |
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| author | Zhenmeng Lin Zhenmeng Lin Hao He Mingfang Yan Xiamei Chen Hanshen Chen Jianfang Ke |
| author_facet | Zhenmeng Lin Zhenmeng Lin Hao He Mingfang Yan Xiamei Chen Hanshen Chen Jianfang Ke |
| author_sort | Zhenmeng Lin |
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| description | BackgroundPostoperative 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 1 month after esophagectomy.MethodsA 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.ResultsThe 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.ConclusionBoth 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. |
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| institution | OA Journals |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Nutrition |
| spelling | doaj-art-2fcf4e64ce8046caa9cba7b5277b53322025-08-20T02:35:59ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-06-011210.3389/fnut.2025.16064701606470Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patientsZhenmeng Lin0Zhenmeng Lin1Hao He2Mingfang Yan3Xiamei Chen4Hanshen Chen5Jianfang Ke6Department of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital, Fuzhou, ChinaDepartment of Anesthesiology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, ChinaDepartment of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital, Fuzhou, ChinaDepartment of Anesthesiology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, ChinaDepartment of Operation, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital, Fuzhou, Fujian, ChinaDepartment of Thoracic Oncology Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, ChinaDepartment of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital, Fuzhou, ChinaBackgroundPostoperative 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 1 month after esophagectomy.MethodsA 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.ResultsThe 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.ConclusionBoth 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.https://www.frontiersin.org/articles/10.3389/fnut.2025.1606470/fullesophageal cancerpostoperative malnutritionmachine learningnomogramsurgery |
| spellingShingle | Zhenmeng Lin Zhenmeng Lin Hao He Mingfang Yan Xiamei Chen Hanshen Chen Jianfang Ke Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients Frontiers in Nutrition esophageal cancer postoperative malnutrition machine learning nomogram surgery |
| title | Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients |
| title_full | Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients |
| title_fullStr | Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients |
| title_full_unstemmed | Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients |
| title_short | Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients |
| title_sort | machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients |
| topic | esophageal cancer postoperative malnutrition machine learning nomogram surgery |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1606470/full |
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