Abstract
PURPOSE The purpose of this paper was to distinguish solid pseudopapillary neoplasms (SPNs) and nonfunctional neuroendocrine tumors (nf-NETs) of pancreas using univariate analysis and clinical-CT logistic regression model. METHODS Twenty-eight patients with SPNs and 46 patients with nf-NETs underwent enhanced CT examinations. Clinical data (sex, age), categorical (location, cystic degeneration, calcification, hemorrhage, and enhancement pattern), and numeric CT features (lesion long diameter, long/ short diameter ratio, tumor attenuation values and tumor/pancreas attenuation ratios at unenhanced phase [UP], arterial phase [AP], and venous phase [VP]) were recorded. The logistic regression model was constructed by stepwise forward method of binary logistic regression after univariate analysis. The corresponding operating characteristic curve (ROC) and nomogram were delineated. The area under the curve (AUC), sensitivity, and specificity of ROC were calculated. RESULTS The SPNs were observed more often in relatively young (P < .001), female (P < .001) patients. After the univariate analysis, the categorical CT features of location (P = .048), hemorrhage (P = .003), and enhancement pattern (P = .004) and the numeric CT features of lesion long diameter (P = .005), tumor/pancreasUP (P = .002), tumorAP (P < .001), and tumor/pancreasAP (P < .001) had statistical significance. The AUC (95% CI), sensitivity, and specificity of a logistic regression model composed of age, tumor/pancreasUP, and tumor/pancreasAP were 0.933 (95% CI, 0.850-0.978), 84.78%, and 92.86%. CONCLUSION The SPNs often occurred in 20- to 40-year-old female patients, were located in the body or tail of pancreas, showed hemorrhagic degeneration, heterogeneous enhancement, and were relatively larger in size compared with nf-NETs. Tumor/pancreasUP, tumorAP, and tumor/pancreasAP values of SPNs were smaller than those of nf-NETs. The clinical-CT logistic regression model and nomogram consisting of age, tumor/pancreasUP, and tumor/pancreasAP parameters helped to differentiate SPNs from nf-NETs.