该研究旨在调查机器学习算法对局部晚期胃癌（LAGC）大网膜转移的预测能力，并比较各种机器学习预测模型的性能指标。对 478 例经病理证实的 LAGC 患者进行回顾性收集，包括临床特征和动脉期计算机断层扫描图像。使用 3D Slicer 软件提取放射组学特征。通过套索回归进一步过滤临床和放射组学特征。选择的临床和放射组学特征用于使用支持向量机（SVM）、决策树（DT）、随机森林（RF）、K-最近邻（KNN）和逻辑回归（LR）构建大网膜转移预测模型。模型的性能指标包括准确性、受试者工作特征曲线的曲线下面积 (AUC)、灵敏度、特异性、阳性预测值 (PPV) 和阴性预测值 (NPV)。在训练队列中，RF预测模型在准确性、AUC、敏感性、特异性、PPV和NPV方面超越了LR、SVM、DT和KNN。与其他四种预测模型相比，RF 模型显着提高了 PPV。在测试队列中，所有五个机器学习预测模型都表现出较低的 PPV。与其他模型相比，DT 模型表现出最显着的性能指标变化，灵敏度为 0.231，特异性为 0.990。与其他四个模型相比，基于 LR 的预测模型的 PPV 最低，为 0.210。在外部验证队列中，预测模型的性能指标总体上与测试队列中的一致。基于 LR 的预测网膜转移模型表现出较低的 PPV。在机器学习算法中，相对于 LR、SVM、KNN 和 DT 模型，RF 预测模型表现出更高的准确性和改进的 PPV。© 2024。作者。

The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models' performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.© 2024. The Author(s).