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Performance of quantitative CT texture analysis in differentiation of gastric tumors

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Japanese Journal of Radiology Aims and scope Submit manuscript

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.

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Funding

The authors received no financial support for the research, authorship, and publication of this article.

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Authors and Affiliations

  1. Radiology Department, Ardahan Devlet Hastanesi, 75000, Ardahan, Turkey

    Tolga Zeydanli

  2. Radiology Department, Gazi University School of Medicine, 06500, Ankara, Turkey

    Huseyin Koray Kilic

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  1. Tolga Zeydanli
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  2. Huseyin Koray Kilic
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Correspondence to Tolga Zeydanli.

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Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol 40, 56–65 (2022). https://doi.org/10.1007/s11604-021-01181-x

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  • DOI: https://doi.org/10.1007/s11604-021-01181-x

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