Performance of quantitative CT texture analysis in differentiation of gastric tumors


Zeydanli T., KILIÇ H. K.

JAPANESE JOURNAL OF RADIOLOGY, vol.40, no.1, pp.56-65, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1007/s11604-021-01181-x
  • Journal Name: JAPANESE JOURNAL OF RADIOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Page Numbers: pp.56-65
  • Keywords: Stomach tumors, Gastric cancer, Texture analysis, Computed tomography, GRAY-LEVEL DISCRETIZATION, CONTRAST-ENHANCED CT, RADIOMIC FEATURES, CANCER, HETEROGENEITY, METASTASIS, ACCURACY, PATTERN
  • Gazi University Affiliated: Yes

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.