比较基于单层二维 (2D) 和全体积三维 (3D) 计算机断层扫描 (CT) 放射组学模型在预测食管鳞状细胞癌 (ESCC) 淋巴血管侵犯 (LVI) 状态方面的性能）。224 名 ESCC 患者（158 名 LVI 缺失和 66 名 LVI 存在）参与了这项回顾性研究。入组患者按 7:3 的比例随机分为训练组和测试组。 2D 和 3D 放射组学特征源自原发性肿瘤的 2D 和 3D 感兴趣区域 (ROI)，使用 1.0 mm 厚度的对比增强 CT (CECT) 图像。采用类间/类内相关系数（ICC）分析、Wilcoxon秩和检验、Spearman相关检验、最小绝对收缩和选择算子筛选2D和3D放射组学特征，并通过多元Logistic回归建立放射组学模型逐步回归。 2D 和 3D 放射组学模型的性能通过受试者工作特征 (ROC) 曲线下面积进行评估。通过决策曲线分析（DCA）评估2D和3D放射组学模型的实际临床效用。2D ROI有753个放射组学特征，3D ROI有1130个放射组学特征，最后保留7个特征来构建2D和3D放射组学模型，分别。 ROC 分析显示，在训练和测试集中，3D 放射组学模型的 AUC 值均高于 2D 放射组学模型（分别为 0.930 与 0.852 和 0.897 与 0.851）。在训练和测试集中，3D 放射组学模型显示出比 2D 放射组学模型更高的准确性（分别为 0.899 与 0.728 和 0.788 与 0.758）。此外，3D放射组学模型具有更高的特异性和阳性预测值，而2D放射组学模型具有更高的敏感性和阴性预测值。 DCA 表明，3D 放射组学模型在总体净效益方面比 2D 放射组学模型提供了更高的实际临床效用。2D 和 3D 放射组学特征都可以用作预测 ESCC 中 LVI 的潜在生物标志物。对于 ESCC 中 LVI 的预测，3D 放射组学模型的性能优于 2D 放射组学模型。© 2024。作者。

To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).Two hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors' 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).There were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.Both 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.© 2024. The Author(s).