Spectral Computed Tomography Imaging in the Differential Diagnosis of Lung Cancer and Inflammatory Myofibroblastic Tumor

Objective The aim of this study was to explore the value of spectral computed tomography (CT) imaging in differentiating lung cancer from inflammatory myofibroblastic tumor (IMT). Methods One hundred twelve patients with 96 lung cancers and 16 IMTs underwent spectral CT during arterial phase (AP) and venous phase (VP). The normalized iodine concentration in AP (NICAP) and VP (NICVP), slope of spectral Hounsfield unit curve in AP (lambda(AP)) and VP (lambda(VP)), and normalized iodine concentration difference between AP and VP (ICD) were calculated. The 2-sample t test compared quantitative parameters. Two readers qualitatively assessed lesion types according to imaging features. Receiver operating characteristic curves were generated to calculate sensitivity and specificity. Sensitivity and specificity of the qualitative and quantitative studies were compared. Results The patients with IMT had significantly higher NICAP, NICVP, lambda(AP), lambda(VP), and ICD than did the patients with lung cancer (P < 0.05). The threshold NICVP of 0.425 would yield the highest sensitivity and specificity of 92.7% and 81.3%, respectively, for differentiating lung cancer from IMT. The logistic regression model produced from combining quantitative parameters NICAP, NICVP, lambda(AP), and lambda(VP) provided a sensitivity and specificity of 100% and 81.3%, respectively, for differentiating lung cancer from IMT. Conclusions Spectral CT imaging with the quantitative analysis may help to increase the accuracy of differentiating lung cancer from IMT.