To investigate the value of tumor morphologic features of pT1-2 gastric cancer (GC) on contrast-enhanced computed tomography (CT) in assessing lymph node metastasis (LNM) with reference to histopathological results. METHODSEighty-six patients seen from October 2017 to April 2019 with pT1-2 GC proven by histopathology were included. Tumor volume and CT densities were measured in the plain scan and the portal-venous phase (PVP), and the percent enhancement was calculated. The correlations between tumor morphologic features and the N stages were analyzed. The diagnostic capability of tumor volume and enhancement features in predicting the LN status of pT1-2 GCs was further investigated using receiver operating characteristic (ROC) analysis. RESULTSTumor volume, CT density in the PVP, and tumor percent enhancement in the PVP correlated significantly with the N stage (rho: 0.307, 0.558, and 0.586, respectively). Tumor volumes were significantly lower in the LNM− group than in the LNM+ group (14.4 mm 3 vs. 22.6 mm 3 , P = 0.004). The differences between the LNM− and LNM+ groups in the CT density in the PVP and the percent enhancement in the PVP were also statistically significant (68.00 HU vs. 87.50 HU, P < 0.001; and 103.06% vs. 179.19%, P < 0.001, respectively). The area under the ROC curves for identifying the LNM+ group was 0.69 for tumor volume and 0.88 for percent enhancement in the PVP, respectively. The percent enhancement in the PVP of 145.2% and tumor volume of 17.4 mL achieved good diagnostic performance in determining LNM+ (sensitivity: 71.4%, 82.1%; specificity: 91.4%, 58.6%; and accuracy: 84.9%, 66.3%, respectively). CONCLUSIONTumor volume and percent enhancement in the PVP of pT1-2 GC could improve the diagnostic accuracy of LNM and would be helpful in image surveillance of these patients. KEYWORDSComputed tomography, contrast enhancement, gastric cancer, lymph node metastasis, tumor volume You may cite this article as: Wang Z, Liu Q, Zhuang X, et al. pT1-2 gastric cancer with lymph node metastasis predicted by tumor morphologic features on contrast-enhanced computed tomography.