Visceral medicine · 2025
Computed Tomography Texture Analysis of Lymph Nodes for Improved Staging Accuracy in Patients with Colon Cancer.
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Leonhardi Jakob, Mehdorn Matthias, Stelzner Sigmar, Scheuermann Uwe, Höhn Anne-Kathrin, Seehofer Daniel, Denecke Timm, Meyer Hans-Jonas
Аннотация
INTRODUCTION: Texture analysis can provide quantitative imaging markers and better characterize tumor tissue in oncological imaging. The present analysis investigated the diagnostic benefit of computed tomography (CT)-derived texture analysis to categorize and stage lymph nodes in patients with colon cancer.
METHODS: In this study, 85 patients were included (n = 39 females, 45.9%) with a mean age of 70.3 ± 14.8 years. All patients were surgically resected, and the lymph nodes were histopathologically analyzed. All investigated lymph nodes were further investigated with texture analysis using the MaZda package.
RESULTS: Out of a total of 279 extracted CT texture features, 7 parameters independently showed statistically significant differences between lymph node positive to negative ones. For instance, the texture parameter S(1,0)AngScMom showed statistically significant differences regarding lymph node metastasis status (0.007 ± 0.004 for N0 vs. 0.005 ± 0.001 for N1-2, p = 0.001). A multivariate model was developed based on n = 7 independent texture parameters. The diagnostic accuracy reached an area under the curve of 0.79 (95% CI: 0.69-0.89) with a sensitivity of 0.77 and a specificity of 0.70, resulting in an accuracy of 0.73.
DISCUSSION: Texture analysis can improve the diagnostic accuracy for nodal CT staging in patients with colon cancer. Further validation studies are needed to confirm the present results.
METHODS: In this study, 85 patients were included (n = 39 females, 45.9%) with a mean age of 70.3 ± 14.8 years. All patients were surgically resected, and the lymph nodes were histopathologically analyzed. All investigated lymph nodes were further investigated with texture analysis using the MaZda package.
RESULTS: Out of a total of 279 extracted CT texture features, 7 parameters independently showed statistically significant differences between lymph node positive to negative ones. For instance, the texture parameter S(1,0)AngScMom showed statistically significant differences regarding lymph node metastasis status (0.007 ± 0.004 for N0 vs. 0.005 ± 0.001 for N1-2, p = 0.001). A multivariate model was developed based on n = 7 independent texture parameters. The diagnostic accuracy reached an area under the curve of 0.79 (95% CI: 0.69-0.89) with a sensitivity of 0.77 and a specificity of 0.70, resulting in an accuracy of 0.73.
DISCUSSION: Texture analysis can improve the diagnostic accuracy for nodal CT staging in patients with colon cancer. Further validation studies are needed to confirm the present results.