Zhang Zhengping, Mi Kede, Wang Zhaojun, Yang Xiaoyan, Meng Shuping, Tian Xingcang, Han Yanzhu, Qu Yuling, Zhu Li, Chen Juan
Department of Key Laboratory of Ningxia Stem Cell and Regenerative Medicine, Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, China.
Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China.
Insights Imaging. 2025 Mar 22;16(1):67. doi: 10.1186/s13244-025-01933-7.
To develop and externally validate an integrated model that utilizes optimized radiomics features from non-contrast-enhanced CT (NE-CT) or contrast-enhanced CT (CE-CT), along with morphological features and clinical risk factors, to predict histological classifications of thymic epithelial tumors (TETs).
A total of 182 patients with TET, classified as the low-risk group and the high-risk group based on histology, were divided into a training cohort (N = 122, center 1) and an external validation cohort (N = 60, center 2). Radiomics features were extracted from different CT types, followed by feature selection, including consistency, correlation, and importance tests, to generate Rad-scores for both NE-CT and CE-CT. The integrated model was developed by combining the optimal Rad-score, morphological features, and clinical risk factors using multivariate logistic regression. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. A nomogram was used to visually present the integrated model.
A total of 851 radiomics features were extracted, with NE-CT and CE-CT Rad-scores consisting of four and five features, respectively. The AUCs of the CE-CT Rad-score were higher than those of the NE-CT Rad-score in both the training cohort (0.783 vs 0.749) and the external validation cohort (0.775 vs 0.723, p = 0.361). The integrated model, combining five morphological features and the CE-CT Rad-score, achieved AUCs of 0.814 and 0.802 in the training and external validation cohorts, respectively.
The integrated model, incorporating radiomics features from CE-CT and morphological features, can help to identify the histological classifications of TETs.
This study developed an integrated model based on radiomics features from contrast-enhanced CT and morphological features, demonstrating that the integrated model has impressive predictive capability in distinguishing histological classifications of thymic epithelial tumors through external validation.
Radiomics features extracted from CT more effectively represented thymic epithelial tumor (TET) heterogeneity than morphological features. The radiomics model using contrast-enhanced CT outperformed that using non-contrast-enhanced CT in identifying histological classifications of TET. The integrated model, combining radiomics and morphological features, exhibited the highest performance in predicting TET histological classifications.
开发并外部验证一种综合模型，该模型利用非增强CT（NE-CT）或增强CT（CE-CT）的优化影像组学特征，以及形态学特征和临床危险因素，来预测胸腺上皮肿瘤（TET）的组织学分类。
总共182例TET患者，根据组织学分为低风险组和高风险组，被分为训练队列（N = 122，中心1）和外部验证队列（N = 60，中心2）。从不同类型的CT中提取影像组学特征，随后进行特征选择，包括一致性、相关性和重要性测试，以生成NE-CT和CE-CT的Rad分数。通过多变量逻辑回归结合最佳Rad分数、形态学特征和临床危险因素来开发综合模型。通过受试者操作特征曲线（AUC）下的面积评估模型性能，并通过德龙检验进行比较。使用列线图直观呈现综合模型。
总共提取了851个影像组学特征，NE-CT和CE-CT的Rad分数分别由4个和5个特征组成。在训练队列（0.783对0.749）和外部验证队列（0.775对0.723，p = 0.361）中，CE-CT的Rad分数的AUC均高于NE-CT的Rad分数。结合5个形态学特征和CE-CT的Rad分数的综合模型，在训练队列和外部验证队列中的AUC分别达到0.814和0.802。
结合CE-CT的影像组学特征和形态学特征的综合模型，有助于识别TET的组织学分类。
本研究基于增强CT的影像组学特征和形态学特征开发了一种综合模型，通过外部验证表明该综合模型在区分胸腺上皮肿瘤的组织学分类方面具有令人印象深刻的预测能力。
从CT中提取的影像组学特征比形态学特征更有效地代表了胸腺上皮肿瘤（TET）的异质性。在识别TET的组织学分类方面，使用增强CT的影像组学模型优于使用非增强CT的模型。结合影像组学和形态学特征的综合模型在预测TET组织学分类方面表现出最高性能。
