Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China.
Department of Radiotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China.
Abdom Radiol (NY). 2024 Jan;49(1):288-300. doi: 10.1007/s00261-023-04070-1. Epub 2023 Oct 16.
To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC).
398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated.
1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations.
The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI.
评估二维（2D）和三维（3D）计算机断层扫描（CT）放射组学分析在食管鳞状细胞癌（ESCC）T 分期中的应用。
398 例经病理证实的 ESCC 患者分为训练集和测试集。所有患者术前均行胸部 CT 扫描。对于每个肿瘤，基于 CT 图像，在最大横截面上勾画 2D 感兴趣区（ROI），并在肿瘤的每个层面上逐层勾画 3D ROI。使用放射组学平台进行特征提取。对于特征选择，采用逐步逻辑回归。采用受试者工作特征（ROC）曲线评估 2D 放射组学模型与 3D 放射组学模型的诊断性能。采用 DeLong 检验比较差异。评估两种放射组学模型的临床实用价值。
提取了 1595 个放射组学特征。经过筛选，构建了两个放射组学模型。在训练集中，2D 放射组学模型（AUC=0.831）和 3D 放射组学模型（AUC=0.830）的曲线下面积（AUC）差异无统计学意义（p=0.973）。在测试集中，2D 放射组学模型（AUC=0.807）和 3D 放射组学模型（AUC=0.797）的 AUC 差异也无统计学意义（p=0.748）。在临床情况下，2D 模型与 3D 模型同样有用。
2D 放射组学模型在区分 ESCC 的 T1-2 期和 T3-4 期方面的性能与 3D 放射组学模型相当。此外，由于 ROI 分割所需的时间较短，2D 放射组学模型可能是更可行的选择。
