Predicting Masaoka-Koga Clinical Stage of Thymic Epithelial Tumors Using Preoperative Spectral Computed Tomography Imaging

ObjectivesTo investigate the utility of spectral computed tomography (CT) parameters for the prediction of the preoperative Masaoka-Koga stage of thymic epithelial tumors (TETs).Materials and MethodsFifty-four patients with TETs, aged from 37 to 73 years old, an average age of 55.56 ± 9.79 years, were included in the study.According to the Masaoka-Koga staging method, there were 19 cases of stage I, 15 cases of stage II, 8 cases of stage III, and 12 cases of stage IV disease. All patients underwent dual-phase enhanced energy spectral CT scans. Regions of interest (ROIs) were defined in sections of the lesion with homogeneous density, the thoracic aorta at the same level as the lesion, the outer fat layer of the lesion, and the anterior chest wall fat layer. The single-energy CT value at 40-140 keV, iodine concentration, and energy spectrum curve of all lesion and thoracic aorta were obtained. The energy spectrum CT parameters of the lesions, extracapsular fat of the lesions, and anterior chest wall fat in stage I and stage II were obtained. The energy spectrum CT parameters of the lesions, enlarged lymph nodes and intravascular emboli in the 3 groups were obtained. The slope of the energy spectrum curve and the normalized iodine concentration were calculated.ResultsIn stage I lesions, there was a statistically significant difference between the slope of the energy spectrum curve for the lesion and those of the fat outside the lesion and the anterior chest wall in the arteriovenous phase (P0.05). The energy spectrum curve of the tumor parenchyma was consistent with that of the enlarged lymph nodes and intravascular emboli. The two radiologists have strong consistency in evaluating TETs Masaoka-Koga staging, The Kappa coefficient is 0.873,(95%CI:0.768-0.978).ConclusionSpectral CT parameters, especially the energy spectrum curve and slope, are valuable for preoperative TET and can be used in preoperative staging prediction.