Computed tomographic features of adenoid cystic carcinoma in the palate

Abstract

Abstract Background To evaluate the computed tomographic features and create a prediction model for clinical diagnosis of adenoid cystic carcinoma (ACC) in the palate with intact mucosa. Methods From March 2016 to May 2018, 102 patients with palatal tumors and intact mucosa, including 28 patients with a pathological diagnosis of ACC after surgery, were enrolled in this study. The patients’ clinical symptoms, computed tomographic features and pathological diagnoses were recorded and analyzed. Independent predictors of ACC were determined by using univariate analysis and multivariate logistic regression, and the discrimination and calibration of the prediction model was evaluated, and internal validation was performed. Results Univariate analysis of patients showed that ACC patients were more likely than non-ACC patients to be older (P = 0.019); to have palatine bone destruction (P<0.001) and greater palatine foramen (GPF) enlargement (P<0.001); to have involvement of the pterygopalatine fossa (P<0.001), foramen rotundum (P<0.001), nasal cavity (P<0.001) and maxillary bone (P<0.001); and to have numbness (P = 0.007) and pain (P<0.001). Multivariate logistic analysis showed that age and GPF enlargement were independent predictors of ACC in palatal tumors. The diagnostic prediction model showed good discrimination and calibration, as evaluated by the area under the receiver operating characteristic curve (0.98) and the Hosmer-Lemeshow goodness-of-fit test (P = 0.927). Conclusions The palate ACC prediction model based on age and GPF enlargement shows excellent discrimination with no evidence of poor calibration. Older patients with palatal tumors and intact mucosa should be considered for ACC when they have GPF enlargement.

Keywords

• Palate
• Adenoid cystic carcinoma
• Salivary gland tumors
• Prediction model
• Computed tomographic features