Radiol Med. 2025 Oct 1. doi: 10.1007/s11547-025-02111-x. Online ahead of print.
ABSTRACT
Thymomas are the most common primary tumors of the anterior mediastinum, frequently associated with paraneoplastic syndromes like myasthenia gravis. This preliminary study investigated the correlation between radiomic features extracted from venous-phase CT images, histological grading (WHO), and disease staging (Masaoka-Koga and TNM) in patients with thymomas. A total of 37 patients were analyzed, with 107 radiomic features extracted using PyRadiomics module. Statistical analysis revealed 11 significant radiomic features distinguishing early and advanced thymomas according to Masaoka-Koga/TNM staging (p < 0.05), with shape_Sphericity, shape_Maximum3DDiameter, and firstorder_Skewness being the most predictive. For WHO classification, 7 significant features differentiated low-risk and high-risk thymomas (p < 0.05), with shape_Sphericity, firstorder-Range, and firstorder_RootMeanSquared showing the highest performance. LASSO models demonstrated high accuracy, with an AUC of 0.9 for Masaoka-Koga/TNM staging and 0.82 for WHO classification. These findings suggest that radiomic features can effectively distinguish thymoma stages and risk levels, potentially aiding in treatment planning and prognosis. By enabling noninvasive tumor characterization, radiomic features could support more personalized treatment strategies and improve decision-making in clinical practice.
PMID:41032229 | DOI:10.1007/s11547-025-02111-x
