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

- Using mean0u and entropy6u in combination, HTET can be differentiated from LTET.
- Using mean0c, HTET can be differentiated from LTET.
- Quantitative texture analysis outperforms subjective visual heterogeneity analysis.

ObjectivesTo investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis.Materials and methodsThe data of 39 patients (mean age, 58.6 ± 14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter. Texture analysis was performed for images with or without a Laplacian of Gaussian filter (with various spatial scaling factors [SSFs]). Two radiologists evaluated the visual heterogeneity of TET using a 3-point scale.ResultsThe mean in the unfiltered image (mean0u) and entropy in the filtered image (SSF: 6 mm) (entropy6u) for UECT, and the mean in the unfiltered image (mean0c) for CECT were significant parameters for differentiating between HTET and LTET as determined by logistic regression analysis. The area under the receiver operating characteristics curve (AUC) for differentiating HTET from LTET using mean0u, entropy6u, and mean0c was 0.75, 0.76, and 0.89, respectively. And the combination of mean0u and entropy6u allowed AUC of 0.87. Entropy6u provided a higher diagnostic performance compared with visual heterogeneity analysis (p ≤ 0.018).ConclusionUsing CT quantitative texture analysis, HTET can be differentiated from LTET with a high diagnostic performance.