title: Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging.
abstract: To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features.
  
  
  
  In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T magnetic resonance imaging (MRI). A Mann-Whitney U-test was applied to select features with significant differences between low- and high-grade groups (P < 0.05). Then support vector machine with recursive feature elimination (SVM-RFE) and classification strategy was adopted to find an optimal feature subset and then to establish a classification model for grading.
  
  
  
  A total 102 features were derived from each VOI and among them, 47 candidate features were selected, which showed significant intergroup differences (P < 0.05). By the SVM-RFE method, an optimal feature subset including 22 features was further selected from candidate features. The SVM classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.861, 82.9%, 78.4%, and 87.1%, respectively.
  
  
  
  Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively.
  
  
  
  3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1281-1288.
authors.affiliation: Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Xi
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Xiaopan
authors.initials: X
authors.lastname: Xu
authors.affiliation: Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Tian
authors.affiliation: Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Baojuan
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Yuxia
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Urology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Zengyue
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Departments of Radiology, Computer Science and Biomedical Engineering, State University of New York, Stony Brook, New York, USA.
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
authors.affiliation: Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Guangbin
authors.initials: G
authors.lastname: Cui
authors.affiliation: Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, P.R. China.
authors.forename: Hongbing
authors.initials: H
authors.lastname: Lu
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D011184:Postoperative Period; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D060388:Support Vector Machine; D001743:Urinary Bladder / Q000000981:diagnostic imaging; D001749:Urinary Bladder Neoplasms / Q000000981:diagnostic imaging*
chemical_list: D015415:Biomarkers
keywords: apparent diffusion coefficient; bladder cancer grade; radiomics; support vector machine; texture feature