• Luping Zhao, Hongfeng Xue, Zhanguo Sun, Yueqin Chen, Hao Yu, and Sen Mao.
• Department of Medical Imaging, the Affiliated Hospital of Jining Medical University, Jining, Shandong, China.
• J Coll Physicians Surg Pak. 2023 Apr 1; 33 (4): 369373369-373.

ObjectiveTo determine whether computed tomography (CT) imaging features can be used to differentiate gastric schwannoma (GS) from gastric leiomyoma (GL) and to develop a nomogram as a predictive model.Study DesignRetrospective study. Place and Duration of the Study: Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, Shandong, China, from July 2009 to June 2022.MethodologyClinical and imaging data of 43 patients with GS and 57 patients with GL were analysed retrospectively. The independent factors for differentiating GS and GL were obtained by the logistic regression analysis. Receiver operating characteristic curve (ROC) was plotted, area under curve (AUC) and calibration tests were used to evaluate the diagnostic efficiency of the model.ResultsThe GS group had more females and was older than the GL group (p <0.05). There were statistical differences between the two groups in tumour location, growth mode, LD/SD ratio, necrosis, ulcers, the presence of tumour-associated lymph nodes, enhancement degree, and the HU (Hounsfield units) values of tumour in the venous phase and delayed phase (p <0.05). Logistic regression analysis showed that tumour location, growth mode, LD/SD (long and short diameters) ratio, and the presence of tumour-associated lymph nodes were independent factors in differentiating GS from GL, and a nomogram model was established accordingly. When the model threshold was >0.319, the AUC was 0.987 (95% confidence interval [CI] 0.941~0.999). The sensitivity and specificity were 97.7% and 94.7%, respectively.ConclusionThe proposed nomogram model based on CT imaging features can be used to differentiate GS from GL.Key WordsGastric leiomyoma, Gastric schwannoma, Computed tomography, Diagnosis.

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