Yuan Yu-Hang, Zhang Hui, Xu Wei-Ling, Dong Dong, Gao Pei-Hong, Zhang Cai-Juan, Guo Yan, Tong Ling-Ling, Gong Fang-Chao
1Department of Radiology, The First Hospital of Jilin University, Jilin, China.
2GE Healthcare, China.
Radiol Oncol. 2025 Feb 27;59(1):69-78. doi: 10.2478/raon-2025-0016. eCollection 2025 Mar 1.
This study aimed to develop and validate 2-Dimensional (2D) and 3-Dimensional (3D) radiomics signatures based on contrast-enhanced computed tomography (CECT) images for preoperative prediction of the thymic epithelial tumors (TETs) risk and compare the predictive performance with conventional CT features.
149 TET patients were retrospectively enrolled from January 2016 to December 2018, and divided into high-risk group (B2/B3/TCs, n = 103) and low-risk group (A/AB/B1, n = 46). All patients were randomly assigned into the training (n = 104) and testing (n = 45) set. 14 conventional CT features were collected, and 396 radiomic features were extracted from 2D and 3D CECT images, respectively. Three models including conventional, 2D radiomics and 3D radiomics model were established using multivariate logistic regression analysis. The discriminative performances of the models were demonstrated by receiver operating characteristic (ROC) curves.
In the conventional model, area under the curves (AUCs) in the training and validation sets were 0.863 and 0.853, sensitivity was 78% and 55%, and specificity was 88% and 100%, respectively. The 2D model yielded AUCs of 0.854 and 0.834, sensitivity of 86% and 77%, and specificity of 72% and 86% in the training and validation sets. The 3D model revealed AUC of 0.902 and 0.906, sensitivity of 75% and 68%, and specificity of 94% and 100% in the training and validation sets.
Radiomics signatures based on 3D images could distinguish high-risk from low-risk TETs and provide complementary diagnostic information.
本研究旨在基于增强计算机断层扫描（CECT）图像开发并验证二维（2D）和三维（3D）放射组学特征，用于术前预测胸腺上皮肿瘤（TETs）风险，并将预测性能与传统CT特征进行比较。
回顾性纳入2016年1月至2018年12月的149例TET患者，分为高危组（B2/B3/TCs，n = 103）和低危组（A/AB/B1，n = 46）。所有患者随机分为训练集（n = 104）和测试集（n = 45）。收集14项传统CT特征，并分别从2D和3D CECT图像中提取396项放射组学特征。使用多变量逻辑回归分析建立包括传统、2D放射组学和3D放射组学模型在内的三种模型。通过受试者操作特征（ROC）曲线展示模型的判别性能。
在传统模型中，训练集和验证集的曲线下面积（AUCs）分别为0.863和0.853，敏感性分别为78%和55%，特异性分别为88%和100%。2D模型在训练集和验证集中的AUCs分别为0.854和0.834，敏感性分别为86%和77%，特异性分别为72%和86%。3D模型在训练集和验证集中的AUC分别为0.902和0.906，敏感性分别为75%和68%，特异性分别为94%和100%。
基于3D图像的放射组学特征能够区分高危和低危TETs，并提供补充诊断信息。
