CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors

Jin Liu; Ping Yin; Sicong Wang; Tao Liu; Chao Sun; Nan Hong

doi:10.3389/fonc.2021.628534

CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors

Front Oncol. 2021 Feb 26:11:628534. doi: 10.3389/fonc.2021.628534. eCollection 2021.

Authors

Jin Liu¹, Ping Yin¹, Sicong Wang², Tao Liu¹, Chao Sun¹, Nan Hong¹

Affiliations

¹ Department of Radiology, Peking University People's Hospital, Beijing, China.
² Pharmaceutical Diagnostic Team, GE Healthcare, Shanghai, China.

Abstract

Objectives: This study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs).

Methods: A total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models.

Results: Of the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(P < 0.001). In the validation set, the NECT-based clinical RM (AUC = 0.770 for low-risk thymoma, 0.689 for high-risk thymoma, and 0.783 for thymic carcinoma; ACC = 0.569) performed better than the CECT-based clinical-semantic RM (AUC = 0.785 for low-risk thymoma, 0.576 for high-risk thymoma, and 0.774 for thymic carcinoma; ACC = 0.483).

Conclusions: NECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better.

Advances in knowledge: Radiomics models may be used for the preoperative prediction of the pathological classification of TETs.

Keywords: computed tomography; machine learning; pathologic classification; radiomics; thymic epithelial tumors.