Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors

Area of Science:

• Radiology
• Oncology
• Medical Imaging

Background:

• Thymic epithelial tumors (TETs) are neoplasms of the thymus.
• Differentiating high-risk TET (HTET) from low-risk TET (LTET) is crucial for treatment planning.
• Computed tomography (CT) is a common imaging modality for TET evaluation.

Purpose of the Study:

• To assess the efficacy of CT quantitative texture analysis in distinguishing HTET from LTET.
• To compare the diagnostic performance of texture analysis with visual heterogeneity assessment.

Main Methods:

• Retrospective analysis of CT scans from 39 patients with TET.
• Texture analysis performed on unenhanced CT (UECT) and contrast-enhanced CT (CECT) images.
• Evaluation of texture parameters including mean and entropy, with and without filters, and visual heterogeneity scoring.

Main Results:

• Specific texture parameters (mean0u, entropy6u, mean0c) were significant predictors for differentiating HTET from LTET.
• Area under the receiver operating characteristic curves (AUCs) ranged from 0.75 to 0.89.
• Texture analysis, particularly entropy6u, demonstrated superior diagnostic performance compared to visual assessment (p≤0.018).

Conclusions:

• CT quantitative texture analysis is a valuable tool for differentiating HTET from LTET.
• Texture analysis offers high diagnostic performance in classifying TET risk.
• This technique may aid in non-invasive risk stratification of thymic epithelial tumors.