Texture analysis of computed tomography data using morphologic and metabolic delineation of esophageal cancer-relation to tumor type and neoadjuvant therapy response

• Published in: Diseases of the esophagus 2019-04, Vol.32 (4)
• Main Authors: Zhang, Y-H, Herlin, G, Rouvelas, I, Nilsson, M, Lundell, L, Brismar, T B
• Format: Article
• Language: English
• Subjects: Medicin och hälsovetenskap
• ISSN: 1120-8694; 1442-2050; 1442-2050
• DOI: 10.1093/dote/doy096

The prognostic values of image-based tumor texture analysis based on computed tomography (CT) and of limiting the segmented tumor volume to metabolically active regions using fludeoxyglucose-positron emission tomography (FDG-PET) were studied in 25 patients with esophageal adenocarcinoma and 11 patients with squamous cell carcinoma. The aims of this study are to describe their CT-image-based texture characteristics before and after neoadjuvant therapy and to evaluate whether limiting the examined tumor volume to metabolically active regions detected with FDG-PET image data would further improve their value. Textural parameters (homogeneity, energy, entropy, contrast, and correlation) based on gray-level co-occurrence matrices (GLCM) were calculated for 3D volumes of segmented esophageal tumors before and after neoadjuvant chemotherapy or radiochemotherapy. Histopathological data after surgical resection and textural parameters before and after neoadjuvant treatment were compared using the Mann-Whitney U test. Significant differences in the textural parameters were observed between adenocarcinoma and squamous cell carcinoma for homogeneity, energy, inertia, and correlation. The use of contrast media during scanning resulted in significant differences in homogeneity, energy, entropy, and inertia for adenocarcinoma but not squamous cell carcinoma. There was also a significant difference in all textural parameters between pathological T status for ypT0-ypT2 and ypT3-ypT4 adenocarcinomas, but not in squamous cell carcinoma patients. No additional value was found from using PET image data to aid segmentation of CT images.