title: Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI.
abstract: OBJECTIVE
  This study aims to determine the three-dimensional (3D) texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues, and propose a noninvasive, image-based strategy for bladder tumor differentiation preoperatively.
  
  
  METHODS
  A total of 62 cancerous and 62 wall volumes of interest (VOI) were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer. To better reflect heterogeneous distribution of tumor tissues, 3D high-order derivative maps (the gradient and curvature maps) were calculated from each VOI. Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI. Statistical analysis and recursive feature elimination-based support vector machine classifier (RFE-SVM) was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance.
  
  
  RESULTS
  From each VOI, a total of 58 texture features were derived. Among them, 37 features showed significant inter-class differences ([Formula: see text]). With 29 optimal features selected by RFE-SVM, the classification results namely the sensitivity, specificity, accuracy and area under the curve (AUC) of the receiver operating characteristics were 0.9032, 0.8548, 0.8790 and 0.9045, respectively. By using synthetic minority oversampling technique to augment the sample number of each group to 200, the sensitivity, specificity, accuracy an AUC value of the feature selection-based classification were improved to 0.8967, 0.8780, 0.8874 and 0.9416, respectively.
  
  
  CONCLUSIONS
  Our results suggest that 3D texture features derived from intensity and high-order derivative maps can better reflect heterogeneous distribution of cancerous tissues. Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Xiaopan
authors.initials: X
authors.lastname: Xu
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Tian
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Guopeng
authors.initials: G
authors.lastname: Zhang
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China.
authors.forename: Guangbin
authors.initials: G
authors.lastname: Cui
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Meng
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Yuxia
authors.initials: Y
authors.lastname: Wu
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
authors.forename: Tianshuai
authors.initials: T
authors.lastname: Liu
authors.affiliation: Department of Urology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China.
authors.forename: Zengyue
authors.initials: Z
authors.lastname: Yang
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China. luhb@fmmu.edu.cn.
authors.forename: Hongbing
authors.initials: H
authors.lastname: Lu
mesh_terms: D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D001743:Urinary Bladder / Q000000981:diagnostic imaging*; D001749:Urinary Bladder Neoplasms / Q000000981:diagnostic imaging*
keywords: Bladder cancer; Feature selection; MRI; RFE-SVM; SMOTE; Texture analysis; Tumor differentiation