CT texture analysis in evaluation of thymic tumors and thymic hyperplasia: correlation with the international thymic malignancy interest group (ITMIG) stage and WHO grade.

Abstract

To evaluate the effectiveness of CT texture analysis (CTTA) in (1) differentiating Thymoma (THY) from thymic hyperplasia (TH) (2) low from high WHO grade, and (3) low from high Masaoka Koga (MK)/International Thymic Malignancy Interest Group (ITMIG) stages. After institute ethical clearance, this cross-sectional study analyzed 26 patients (THY-18, TH-8) who underwent dual energy CT (DECT) and surgery between January 2016 and December 2018. CTTA was performed using TexRad (Feedback Medical Ltd., Cambridge, UK- www.fbkmed.com) by a single observer. Free hand regions of interest (ROIs) were placed over axial sections where there was maximum enhancement and homogeneity. Filtration histogram was used to generate six first-order texture parameters [mean, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at six spatial scaling factors "SSF 0, 2, 3, 4, 5, and 6". Mann-Whitney test was applied among various categories and p value < 0.05 was considered significant. Three-step feature selection was performed to determine the best parameters among each category. The best performing parameters were (1) THY vs TH- Mean at "SSF 0" (AUC: 0.8889) and MPP at "SSF 0" (AUC: 0.8889), (2) Low vs high WHO grade - no parameter showed statistical significance with good AUC, and (3) Low vs high MK/ITMIG stage- SD at "SSF 6" (AUC: 0.8052 and 0.8333 respectively]). CTTA revealed several parameters with excellent diagnostic performance in differentiating thymoma from thymic hyperplasia and MK/ITMIG high vs low stages. CTTA could potentially serve as a non-invasive tool for this stratification. This study has employed texture analysis, a novel radiomics method on DECT scans to determine the best performing parameter and their corresponding cut-off values to differentiate among the above-mentioned categories. These new parameters may help add another layer of confidence to non-invasively stratify and prognosticate patients accurately which was only previously possible with a biopsy.