title: Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From Serous Oligocystic Adenomas in Spectral CT Images.
abstract: OBJECTIVE
  This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed computer-aided diagnosis (CAD) scheme to differentiate pancreatic serous oligocystic adenomas (SOAs) from mucinous cystic neoplasms of pancreas cystic lesions.
  
  
  METHODS
  This study was conducted from January 2010 to October 2013. Twenty-three patients (5 men and 18 women; mean age, 43.96 years old) with SOA and 19 patients (3 men and 16 women; mean age, 41.74 years old) with MCN were included in this retrospective study. Two types of features were collected by dual-energy spectral CT imaging as follows: conventional and additional quantitative spectral CT features. Classification results of the CAD scheme were compared using the conventional features and full feature data set. Important features were selected using support vector machine classification method combined with feature-selection technique. The optimal cutoff values of selected features were determined through receiver-operating characteristic curve analyses.
  
  
  RESULTS
  Combining conventional features with additional spectral CT features improved the overall accuracy from 88.37% to 93.02%. The selected features of the proposed CAD scheme were tumor size, contour, location, and low-energy CT values (43 keV). Iodine-water basis material pair densities in both arterial phase (AP) and portal venous phase (PP) were important factors for differential diagnosis of SOA and MCN. The optimal cutoff values of long axis, short axis, 40 keV monochromatic CT value in AP, iodine (water) density in AP, 43 keV monochromatic CT value in PP, and iodine (water) density in PP were 3.4 mm, 3.1 mm, 35.7 Hu, 0.32533 mg/mL, 39.4 Hu, and 0.348 mg/mL, respectively.
  
  
  CONCLUSIONS
  The combination of conventional features and additional information provided by dual-energy spectral CT shows a high accuracy in the CAD scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of SOAs and MCNs with machine learning algorithms.
authors.affiliation: Department of Biomedical Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
authors.forename: Xiaozhu
authors.initials: X
authors.lastname: Lin
authors.affiliation: Department of Biomedical Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Chun
authors.initials: C
authors.lastname: Hui
authors.affiliation: Department of Electronic and Information Engineering, Centre for Signal Processing, the Hong Kong Polytechnic University, Hong Kong, China.
authors.forename: Kin Man
authors.initials: KM
authors.lastname: Lam
authors.affiliation: Department of Biomedical Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China suzhang@sjtu.edu.cn.
authors.forename: Su
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000236:Adenoma / Q000000981:diagnostic imaging*; D000328:Adult; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018297:Neoplasms, Cystic, Mucinous, and Serous / Q000000981:diagnostic imaging*; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging*; D025341:Principal Component Analysis; D012372:ROC Curve; D012189:Retrospective Studies; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed
keywords: computer-aided diagnosis; dual-energy spectral CT; mucinous cystic neoplasm; serous oligocystic adenoma; support vector machine