Contrast-enhanced computed tomography-based radiomics models for the risk categorization of thymoma

• Published in: Zhongguo aizheng zazhi
• Main Author: JIANG Tiaoyan, JIA Tianying, ZHANG Qin
• Format: Article
• Language: English
• Published: Editorial Office of China Oncology 2024-06-01
• Subjects: |mediastinal|thymoma|computed tomography|machine learning
• Online Access: http://www.china-oncology.com/fileup/1007-3639/PDF/1721112112497-1732976443.pdf

Background and purpose: Preoperative risk categorization of thymoma is useful for treatment decisions but remains challenging. This study focused on training radiomics models using contrast-enhanced computed tomography (CECT) images for thymoma risk categorization and validating the model's performance, reliability and generalizability in a relatively large cohort. Methods: This retrospective cohort study analyzed the clinical data of thymoma patients (Masaoka Koga Ⅰ-Ⅲ) who underwent thymectomy surgery at the Affiliated Chest Hospital of Shanghai Jiao Tong University School of Medicine from January 2008 to December 2017. The cohort was divided into a training group (80%) and a test group (20%) using stratified random selection. The gold standard for histologic types was based on surgically resected specimens. Low-risk histologic types included A, AB and B1. High-risk histologic types included B2 and B3. Radiomics features were extracted from manually segmented regions of interest on preoperative CECT images. Interobserver correlation and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection. Model performance metrics included area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. Clinical characteristics were added to the combined model. Results: A total of 478 patients (mean age 51.3±12.3 years, 48.1% was male) were included. The AUC of the clinical model, the CECT-based model, and the model using both clinical and CECT features on the test set were 0.666, 0.831 and 0.850, respectively. The best performing model had a sensitivity of 0.829 and a specificity of 0.764. Conclusion: CECT-based radiomics models showed good performance in risk categorization of thymomas.