title: Quantitative glioma grading using transformed gray-scale invariant textures of MRI.
abstract: A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings.
  
  
  
  In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement.
  
  
  
  The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334.
  
  
  
  More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.
authors.affiliation: Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Research Center of Translational Imaging, College of Medicine, Taipei Medical University, Taipei, Taiwan.
authors.forename: Kevin
authors.initials: K
authors.lastname: Li-Chun Hsieh
authors.affiliation: Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Research Center of Translational Imaging, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan. Electronic address: sandy0932@gmail.com.
authors.forename: Cheng-Yu
authors.initials: CY
authors.lastname: Chen
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. Electronic address: buddylo@tmu.edu.tw.
authors.forename: Chung-Ming
authors.initials: CM
authors.lastname: Lo
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D005910:Glioma / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D060787:Neoplasm Grading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Brain tumor; Computer-aided diagnosis; Glioma; Local binary pattern; Magnetic resonance imaging