Li Yanqi, Su Yang, Shao Shengli, Wang Tao, Liu Xiaokun, Qin Jichao
Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
Surgery. 2025 Apr;180:108999. doi: 10.1016/j.surg.2024.108999. Epub 2024 Dec 27.
Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence of duodenal stump leakage in patients who underwent laparoscopic gastrectomy for gastric cancer.
The present study included the data of 4,070 patients with gastric adenocarcinoma who received laparoscopic gastrectomy. Five algorithms, namely, k-nearest neighbors, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were used to establish models with the preoperative and intraoperative clinical features of patients. Performance assessment was carried out to determine the optimal model.
The present study involved 4,070 patients and incorporated 11 clinicopathologic features to construct machine learning models (males, 2,688, 66.0%; females, 1,382, 34.0%; age, 58 ± 11 years). Among the 5 algorithms, the support vector machine model exhibited the optimal performance, with an area under the curve of 0.866 (95% confidence interval, 0.803-0.928), sensitivity of 0.806, accuracy of 0.821, and specificity of 0.821. The analysis using the support vector machine model revealed that tumor location and clinic tumor stage significantly contributed to duodenal stump leakage.
The support vector machine model independently predicted duodenal stump leakage in patients with gastric cancer and exhibited favorable discrimination and accuracy. Thus, the construction of an efficient and intuitive online predictive tool demonstrated that the support vector machine model may exhibit potential in the prevention and adjunctive treatment of duodenal stump leakage. The model indicates that besides tumor location and stage, operation time, preoperative pyloric obstruction, and patient age also are important factors that have a significant impact on the occurrence of duodenal stump leakage after surgery.
十二指肠残端漏是胃切除术后最严重的并发症之一，死亡率很高。本研究旨在建立一种基于机器学习的预测模型，用于预测接受腹腔镜胃癌切除术患者发生十二指肠残端漏的情况。
本研究纳入了4070例接受腹腔镜胃癌切除术的胃腺癌患者的数据。使用五种算法，即k近邻算法、逻辑回归、随机森林、支持向量机和极端梯度提升算法，根据患者术前和术中的临床特征建立模型。进行性能评估以确定最佳模型。
本研究共纳入4070例患者，并纳入11项临床病理特征来构建机器学习模型（男性2688例，占66.0%；女性1382例，占34.0%；年龄58±11岁）。在这5种算法中，支持向量机模型表现出最佳性能，曲线下面积为0.866（95%置信区间，0.803-0.928），灵敏度为0.806，准确率为0.821，特异度为0.821。使用支持向量机模型分析发现，肿瘤位置和临床肿瘤分期对十二指肠残端漏有显著影响。
支持向量机模型可独立预测胃癌患者的十二指肠残端漏，具有良好的区分度和准确性。因此，构建一个高效直观的在线预测工具表明，支持向量机模型在十二指肠残端漏的预防和辅助治疗中可能具有潜力。该模型表明，除肿瘤位置和分期外，手术时间、术前幽门梗阻和患者年龄也是对术后十二指肠残端漏发生有显著影响的重要因素。
