Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis

Objective: We aimed to quantitatively assess the parotid gland by using computed tomography (CT) texture analysis to detect parotid sialadenitis (PS). Study Design: This retrospective case-control study included 43 patients with PS who underwent CT and magnetic resonance imaging (MRI). Parotid glands with an abnormal signal (STIR: High) on MRI were identified as showing PS. Patients with parotid gland tumors, bilateral PS, marked fatty degeneration, and severe artifacts on CT were excluded. The texture features of parotid glands with PS and the contralateral normal parotid glands were analyzed using the open-access software LIFEx. The regions of interest were manually placed by tracing contours of both parotid glands on CT images. The results were tested with the paired t-test (or Wilcoxon rank-sum test when appropriate). Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of texture features to predict PS. Results: Six gray level run length matrix features, 2 neighborhood gray level difference matrix features, and 5 gray level zone length matrix features displayed significant differences between PS and normal glands (P ≤.047). ROC curve analysis showed acceptable accuracy in 4 texture features. Conclusions: CT texture analysis allowed quantitative assessment of parotid glands and may have the potential to detect PS.