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A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

  • Authors:
    • Xiangmeng Chen
    • Bao Feng
    • Changlin Li
    • Xiaobei Duan
    • Yehang Chen
    • Zhi Li
    • Zhuangsheng Liu
    • Chaotong Zhang
    • Wansheng Long

  • View Affiliations

  • Published online on: February 11, 2020     https://doi.org/10.3892/or.2020.7497
  • Pages: 1256-1266

  • Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

In the present study, we aimed to construct a radiomics model using contrast‑enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low‑ and 97 high‑risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast‑enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non‑zero coefficients were used to develop a radiomics score, which significantly differed between low‑ and high‑risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high‑risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627‑0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874‑0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887‑0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision‑curve analysis showed that the combined model added more net benefit than the single‑parameter models. In conclusion, a radiomics signature based on contrast‑enhanced CT has the potential to differentiate between low‑ and high‑risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.
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April-2020
Volume 43 Issue 4

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Spandidos Publications style
Chen X, Feng B, Li C, Duan X, Chen Y, Li Z, Liu Z, Zhang C and Long W: A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography. Oncol Rep 43: 1256-1266, 2020.
APA
Chen, X., Feng, B., Li, C., Duan, X., Chen, Y., Li, Z. ... Long, W. (2020). A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography. Oncology Reports, 43, 1256-1266. https://doi.org/10.3892/or.2020.7497
MLA
Chen, X., Feng, B., Li, C., Duan, X., Chen, Y., Li, Z., Liu, Z., Zhang, C., Long, W."A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography". Oncology Reports 43.4 (2020): 1256-1266.
Chicago
Chen, X., Feng, B., Li, C., Duan, X., Chen, Y., Li, Z., Liu, Z., Zhang, C., Long, W."A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography". Oncology Reports 43, no. 4 (2020): 1256-1266. https://doi.org/10.3892/or.2020.7497