title: Texture-based classification of different gastric tumors at contrast-enhanced CT.
abstract: OBJECTIVE
  To determine the feasibility of texture analysis for the classification of gastric adenocarcinoma, lymphoma, and gastrointestinal stromal tumors on contrast-enhanced hydrodynamic-MDCT images.
  
  
  METHODS
  The arterial phase scans of 47 patients with adenocarcinoma (AC) and a histologic tumor grade of [AC-G1, n=4, G1, n=4; AC-G2, n=7; AC-G3, n=16]; GIST, n=15; and lymphoma, n=5, and the venous phase scans of 48 patients with AC-G1, n=3; AC-G2, n=6; AC-G3, n=14; GIST, n=17; lymphoma, n=8, were retrospectively reviewed. Based on regions of interest, texture analysis was performed, and features derived from the gray-level histogram, run-length and co-occurrence matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher coefficients, probability of classification error, average correlation coefficients, and mutual information coefficients were used to create combinations of texture features that were optimized for tumor differentiation. Linear discriminant analysis in combination with a k-nearest neighbor classifier was used for tumor classification.
  
  
  RESULTS
  On arterial-phase scans, texture-based lesion classification was highly successful in differentiating between AC and lymphoma, and GIST and lymphoma, with misclassification rates of 3.1% and 0%, respectively. On venous-phase scans, texture-based classification was slightly less successful for AC vs. lymphoma (9.7% misclassification) and GIST vs. lymphoma (8% misclassification), but enabled the differentiation between AC and GIST (10% misclassification), and between the different grades of AC (4.4% misclassification). No texture feature combination was able to adequately distinguish between all three tumor types.
  
  
  CONCLUSIONS
  Classification of different gastric tumors based on textural information may aid radiologists in establishing the correct diagnosis, at least in cases where the differential diagnosis can be narrowed down to two histological subtypes.
authors.affiliation: Department of Radiology, Medical University of Vienna, Austria. ahmed.ba-ssalamah@meduniwien.ac.at
authors.forename: Ahmed
authors.initials: A
authors.lastname: Ba-Ssalamah
authors.forename: Dina
authors.initials: D
authors.lastname: Muin
authors.forename: Ruediger
authors.initials: R
authors.lastname: Schernthaner
authors.forename: Christiana
authors.initials: C
authors.lastname: Kulinna-Cosentini
authors.forename: Nina
authors.initials: N
authors.lastname: Bastati
authors.forename: Judith
authors.initials: J
authors.lastname: Stift
authors.forename: Richard
authors.initials: R
authors.lastname: Gore
authors.forename: Marius E
authors.initials: ME
authors.lastname: Mayerhoefer
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007479:Iopamidol / Q000031:analogs & derivatives*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013274:Stomach Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014283:Triiodobenzoic Acids
chemical_list: D014283:Triiodobenzoic Acids; C057937:iomeprol; C044834:iodixanol; D007479:Iopamidol
keywords: GIST; Gastric adenocarcinoma; Gastric lymphoma; Gastric tumors; MDCT; Texture analysis