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Objective <p>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).</p> Methods <p>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 (<i>N</i> = 122, center 1) and an external validation cohort (<i>N</i> = 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.</p> Results <p>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, <i>p</i> = 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.</p> Conclusion <p>The integrated model, incorporating radiomics features from CE-CT and morphological features, can help to identify the histological classifications of TETs.</p> Critical relevance statement <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Radiomics features extracted from CT more effectively represented thymic epithelial tumor (TET) heterogeneity than morphological features.</p> </ItemContent> <ItemContent> <p>The radiomics model using contrast-enhanced CT outperformed that using non-contrast-enhanced CT in identifying histological classifications of TET.</p> </ItemContent> <ItemContent> <p>The integrated model, combining radiomics and morphological features, exhibited the highest performance in predicting TET histological classifications.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Using optimized CT type to predict histological classifications of thymic epithelial tumors: a radiomics integrated analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月22日

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  • Zhengping Zhang,
  • Kede Mi,
  • Zhaojun Wang,
  • Xiaoyan Yang,
  • Shuping Meng,
  • Xingcang Tian,
  • Yanzhu Han,
  • Yuling Qu,
  • Li Zhu,
  • Juan Chen

摘要

Objective

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).

Methods

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.

Results

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.

Conclusion

The integrated model, incorporating radiomics features from CE-CT and morphological features, can help to identify the histological classifications of TETs.

Critical relevance statement

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.

Key Points

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.

Graphical Abstract