title: Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions.
abstract: OBJECTIVE
  The purpose is to develop and evaluate the ability of the computer-aided diagnosis (CAD) methods that apply texture analysis and pattern classification to differentiate malignant and benign bone and soft-tissue lesions on 18F-fluorodeoxy-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) images.
  
  
  METHODS
  Subjects were 103 patients with 59 malignant and 44 benign bone and soft tissue lesions larger than 25 mm in diameter. Variable texture parameters of standardized uptake values (SUV) and CT Hounsfield unit values were three-dimensionally calculated in lesional volumes-of-interest segmented on PET/CT images. After selection of a subset of the most optimal texture parameters, a support vector machine classifier was used to automatically differentiate malignant and benign lesions. We developed three kinds of CAD method. Two of them utilized only texture parameters calculated on either CT or PET images, and the other one adopted the combined PET and CT texture parameters. Their abilities of differential diagnosis were compared with the SUV method with an optimal cut-off value of the maximum SUV.
  
  
  RESULTS
  The CAD methods utilizing only optimal PET (or CT) texture parameters showed sensitivity of 83.05 % (81.35 %), specificity of 63.63 % (61.36 %), and accuracy of 74.76 % (72.82 %). Although the ability of differential diagnosis by PET or CT texture analysis alone was not significantly different from the SUV method whose sensitivity, specificity, and accuracy were 64.41, 61.36, and 63.11 % (the optimal cut-off SUVmax was 5.4 ± 0.9 in the 10-fold cross-validation test), the CAD method with the combined PET and CT optimal texture parameters (PET: entropy and coarseness, CT: entropy and correlation) exhibited significantly better performance compared with the SUV method (p = 0.0008), showing a sensitivity of 86.44 %, specificity of 77.27 %, and accuracy of 82.52 %.
  
  
  CONCLUSIONS
  The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.
authors.affiliation: Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, 1-1 Nojihigashi 1-chome, Kusatsu, Shiga, 525-8577, Japan, qdruixu@gmail.com.
authors.forename: Rui
authors.initials: R
authors.lastname: Xu
authors.forename: Shoji
authors.initials: S
authors.lastname: Kido
authors.forename: Kazuyoshi
authors.initials: K
authors.lastname: Suga
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Hirano
authors.forename: Rie
authors.initials: R
authors.lastname: Tachibana
authors.forename: Keiichiro
authors.initials: K
authors.lastname: Muramatsu
authors.forename: Kazuki
authors.initials: K
authors.lastname: Chagawa
authors.forename: Satoshi
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D019540:Area Under Curve; D001859:Bone Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D001842:Bone and Bones / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003937:Diagnosis, Differential; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods; D049268:Positron-Emission Tomography / Q000379:methods*; D012372:ROC Curve; D019275:Radiopharmaceuticals; D012680:Sensitivity and Specificity; D012983:Soft Tissue Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18