Jpn J Radiol. 2021 Jul 25. doi: 10.1007/s11604-021-01181-x. Online ahead of print.
ABSTRACT
PURPOSE: To examine the computed tomography (CT) images of patients with a diagnosis of gastric tumor by texture analysis and to investigate its place in differential diagnosis.
MATERIALS AND METHODS: Contrast enhanced venous phase CT images of 163 patients with pathological diagnosis of gastric adenocarcinoma (n = 125), gastric lymphoma (n = 12) and gastrointestinal stromal tumors (n = 26) were retrospectively analyzed. Pixel size adjustment, gray-level discretization and gray-level normalization procedures were applied as pre-processing steps. Region of interest (ROI) was determined from the axial slice that represented the largest lesion area and a total of 40 texture features were calculated for each patient. Texture features were compared between the tumor subtypes and between adenocarcinoma grades. Statistically significant texture features were combined into a single parameter by logistic regression analysis. The sensitivity and specificity of these features and the combined parameter were measured to differentiate tumor subtypes by receiver-operating characteristic curve (ROC) analysis.
RESULTS: Classifications between adenocarcinoma versus lymphoma, adenocarcinoma vs. gastrointestinal stromal tumor (GIST) and well-differentiated adenocarcinoma versus poorly differentiated adenocarcinoma using texture features yielded successful results with high sensitivity (98, 91, 96%, respectively) and specificity (75, 77, 80%, respectively).
CONCLUSIONS: CT texture analysis is a non-invasive promising method for classifying gastric tumors and predicting gastric adenocarcinoma differentiation.
PMID:34304383 | DOI:10.1007/s11604-021-01181-x
