Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis

• DOI: 10.1007/s00432-023-05295-9
• Publication Date: 2023-08-27T07:01:40Z
• Authors: Na Feng; Hai-Yan Chen; Yuan-Fei Lu; Yao Pan; Jie-Ni Yu; Xin-Bin Wang; Xue-Ying Deng; Ri-Sheng Yu
• Keywords: Neuroendocrine Tumors; Gastrointestinal Stromal Tumors; Duodenal Neoplasms; Research; Humans; Prognosis; Tomography, X-Ray Computed; 3. Good health; Retrospective Studies

Abstract Objective To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients.

Materials and methods This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan–Meier survival analyses were performed for survival analysis of dNENs (n = 44).

Results Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206–0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053–0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575–20.774). The AUC was 0.866 (95% CI 0.765–0.968), with a sensitivity of 90.91% (95% CI 70.8–98.9%), specificity of 77.78% (95% CI 64.4–88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes.

Conclusion We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.