title: High-Performance CAD-CTC Scheme Using Shape Index, Multiscale Enhancement Filters, and Radiomic Features.
abstract: Computer-aided detection (CAD) systems for computed tomography colonography (CTC) can automatically detect colorectal polyps. The main problem of currently developed CAD-CTC systems is the numerous false positives (FPs) caused by the existence of complicated colon structures (e.g., haustral fold, residual fecal material, inflation tube, and ileocecal valve). This study proposes a CAD-CTC scheme using shape index, multiscale enhancement filters, and radiomic features to address the FP issue.
  
  
  
  Shape index and multiscale enhancement filter calculated in the Gaussian smoothed geodesic distance field are combined to generate the polyp candidates. A total of 440 well-defined radiomic features collected from previous radiomic studies and 200 newly developed radiomic features are used to construct a supervised classification model to reduce the numerous FPs.
  
  
  
  The proposed CAD-CTC scheme was evaluated on 152 oral contrast-enhanced CT datasets from 76 patients with 103 polyps ≥5 mm. The detection results were 98.1% and 95.3% by-polyp sensitivity and per-scan sensitivity, respectively, with the same FP rate of 1.3 FPs per dataset for polyps ≥5 mm.
  
  
  
  Experimental results indicate that the proposed CAD-CTC scheme can achieve high sensitivity while maintaining a low FP rate.
  
  
  
  The proposed CAD-CTC scheme would be a beneficial tool in clinical colon examination.
authors.forename: Yacheng
authors.initials: Y
authors.lastname: Ren
authors.forename: Jingchen
authors.initials: J
authors.lastname: Ma
authors.forename: Junfeng
authors.initials: J
authors.lastname: Xiong
authors.forename: Lin
authors.initials: L
authors.lastname: Lu
authors.forename: Jun
authors.initials: J
authors.lastname: Zhao
mesh_terms: D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D000069553:Supervised Machine Learning