title: Texture-Based Analysis of 100 MR Examinations of Head and Neck Tumors - Is It Possible to Discriminate Between Benign and Malignant Masses in a Multicenter Trial?
abstract: OBJECTIVE
  To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors.
  
  
  METHODS
  The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2 D and 3 D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values.
  
  
  RESULTS
  There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found.
  
  
  CONCLUSIONS
  Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data.
  
  
  CONCLUSIONS
  2 D/3 D texture-based analysis can be performed in head and neck tumors. Texture-based analysis can differentiate between benign and malignant masses. Analyzed MR images should originate from one scanner with an identical protocol.
authors.affiliation: Department of Biomedical Imaging und Image-guided Therapy, Subdivision of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Austria.
authors.forename: J
authors.initials: J
authors.lastname: Fruehwald-Pallamar
authors.affiliation: Department of Radiology, UCSD Medical Center, San Diego, United States.
authors.forename: J R
authors.initials: JR
authors.lastname: Hesselink
authors.affiliation: Department of Radiology, UCSD Medical Center, San Diego, United States.
authors.forename: M F
authors.initials: MF
authors.lastname: Mafee
authors.affiliation: Department of Biomedical Imaging und Image-guided Therapy, Medical University of Vienna, Austria.
authors.forename: L
authors.initials: L
authors.lastname: Holzer-Fruehwald
authors.affiliation: Department of Biomedical Imaging und Image-guided Therapy, Subdivision of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Austria.
authors.forename: C
authors.initials: C
authors.lastname: Czerny
authors.affiliation: Department of Biomedical Imaging und Image-guided Therapy, Medical University of Vienna, Austria.
authors.forename: M E
authors.initials: ME
authors.lastname: Mayerhoefer
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003528:Carcinoma, Adenoid Cystic / Q000000981:diagnostic imaging; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging; D002648:Child; D003937:Diagnosis, Differential; D005260:Female; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008223:Lymphoma / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011859:Radiography; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012984:Software; D055815:Young Adult