Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients
PurposeThis study aimed to develop a machine learning (ML) model for real-time prediction of duodenal stump leakage (DSL) following gastrectomy in patients with gastric cancer (GC) using a comprehensive set of clinical variables to improve postoperative outcomes and monitoring efficiency.MethodsA re...
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| Format: | Article |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Surgery |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1550990/full |
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| author | Jae Hun Chung Jae Hun Chung Jae Hun Chung Yushin Kim Dongjun Lee Dongwon Lim Dongwon Lim Dongwon Lim Sun-Hwi Hwang Sun-Hwi Hwang Sun-Hwi Hwang Si-Hak Lee Si-Hak Lee Si-Hak Lee Woohwan Jung |
| author_facet | Jae Hun Chung Jae Hun Chung Jae Hun Chung Yushin Kim Dongjun Lee Dongwon Lim Dongwon Lim Dongwon Lim Sun-Hwi Hwang Sun-Hwi Hwang Sun-Hwi Hwang Si-Hak Lee Si-Hak Lee Si-Hak Lee Woohwan Jung |
| author_sort | Jae Hun Chung |
| collection | DOAJ |
| description | PurposeThis study aimed to develop a machine learning (ML) model for real-time prediction of duodenal stump leakage (DSL) following gastrectomy in patients with gastric cancer (GC) using a comprehensive set of clinical variables to improve postoperative outcomes and monitoring efficiency.MethodsA retrospective analysis was conducted on 1,107 patients with GC who underwent gastrectomy at Pusan National University Yangsan Hospital between 2019 and 2022. One hundred eighty-nine features were extracted from each patient record, including demographic data, preoperative comorbidities, and blood test outcomes from the subsequent seven postoperative days (POD). Six ML algorithms were evaluated: Logistic Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), and Neural Network (NN). The models predicted DSL occurrence preoperatively and on POD 1, 2, 3, 5, and 7. Performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUROC) and Recall@K.ResultsAmong the 1,107 patients, 29 developed DSL. XGB demonstrated the highest AUROC score (0.880), followed by RF (0.858), LR (0.823), SVM (0.819), NN (0.753), and KNN (0.726). The RF achieved the best Recall@K score of 0.643. Including additional POD features improved the predictive performance, with the AUROC value increasing to 0.879 on POD 7. The confidence scores of the model indicated that the DSL predictions became more reliable over time.ConclusionThe study concluded that ML models, notably the XGB algorithm, can effectively predict DSL in real-time using comprehensive clinical data, enhancing the clinical decision-making process for GC patients. |
| format | Article |
| id | doaj-art-2f1f24c1c9a84ea19cfafcc20d6a3821 |
| institution | OA Journals |
| issn | 2296-875X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj-art-2f1f24c1c9a84ea19cfafcc20d6a38212025-08-20T01:48:57ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2025-05-011210.3389/fsurg.2025.15509901550990Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patientsJae Hun Chung0Jae Hun Chung1Jae Hun Chung2Yushin Kim3Dongjun Lee4Dongwon Lim5Dongwon Lim6Dongwon Lim7Sun-Hwi Hwang8Sun-Hwi Hwang9Sun-Hwi Hwang10Si-Hak Lee11Si-Hak Lee12Si-Hak Lee13Woohwan Jung14Division of Gastrointestinal Surgery, Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaResearch Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Surgery, School of Medicine, Pusan National University, Yangsan, Republic of KoreaDepartment of Applied Artificial Intelligence (Major in Bio Artificial Intelligence), Hanyang University, Ansan, Republic of KoreaDepartment of Applied Artificial Intelligence (Major in Bio Artificial Intelligence), Hanyang University, Ansan, Republic of KoreaDivision of Gastrointestinal Surgery, Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaResearch Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Surgery, School of Medicine, Pusan National University, Yangsan, Republic of KoreaDivision of Gastrointestinal Surgery, Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaResearch Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Surgery, School of Medicine, Pusan National University, Yangsan, Republic of KoreaDivision of Gastrointestinal Surgery, Department of Surgery, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaResearch Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Surgery, School of Medicine, Pusan National University, Yangsan, Republic of KoreaDepartment of Artificial Intelligence, Hanyang University, Ansan, Republic of KoreaPurposeThis study aimed to develop a machine learning (ML) model for real-time prediction of duodenal stump leakage (DSL) following gastrectomy in patients with gastric cancer (GC) using a comprehensive set of clinical variables to improve postoperative outcomes and monitoring efficiency.MethodsA retrospective analysis was conducted on 1,107 patients with GC who underwent gastrectomy at Pusan National University Yangsan Hospital between 2019 and 2022. One hundred eighty-nine features were extracted from each patient record, including demographic data, preoperative comorbidities, and blood test outcomes from the subsequent seven postoperative days (POD). Six ML algorithms were evaluated: Logistic Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), and Neural Network (NN). The models predicted DSL occurrence preoperatively and on POD 1, 2, 3, 5, and 7. Performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUROC) and Recall@K.ResultsAmong the 1,107 patients, 29 developed DSL. XGB demonstrated the highest AUROC score (0.880), followed by RF (0.858), LR (0.823), SVM (0.819), NN (0.753), and KNN (0.726). The RF achieved the best Recall@K score of 0.643. Including additional POD features improved the predictive performance, with the AUROC value increasing to 0.879 on POD 7. The confidence scores of the model indicated that the DSL predictions became more reliable over time.ConclusionThe study concluded that ML models, notably the XGB algorithm, can effectively predict DSL in real-time using comprehensive clinical data, enhancing the clinical decision-making process for GC patients.https://www.frontiersin.org/articles/10.3389/fsurg.2025.1550990/fullduodenal stump leakagegastrectomymachine learninggastric cancerpredictive modeling |
| spellingShingle | Jae Hun Chung Jae Hun Chung Jae Hun Chung Yushin Kim Dongjun Lee Dongwon Lim Dongwon Lim Dongwon Lim Sun-Hwi Hwang Sun-Hwi Hwang Sun-Hwi Hwang Si-Hak Lee Si-Hak Lee Si-Hak Lee Woohwan Jung Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients Frontiers in Surgery duodenal stump leakage gastrectomy machine learning gastric cancer predictive modeling |
| title | Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients |
| title_full | Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients |
| title_fullStr | Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients |
| title_full_unstemmed | Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients |
| title_short | Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients |
| title_sort | machine learning based real time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients |
| topic | duodenal stump leakage gastrectomy machine learning gastric cancer predictive modeling |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1550990/full |
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