title: Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images.
abstract: BACKGROUND
  EUS can detect morphologic abnormalities of pancreatic cancer with high sensitivity but with limited specificity.
  
  
  OBJECTIVE
  To develop a classification model for differential diagnosis of pancreatic cancer by using a digital imaging processing (DIP) technique to analyze EUS images of the pancreas.
  
  
  METHODS
  A retrospective, controlled, single-center design was used.
  
  
  METHODS
  The study took place at the Second Military Medical University, Shanghai, China.
  
  
  METHODS
  There were 153 pancreatic cancer and 63 noncancer patients in this study.
  
  
  METHODS
  All patients underwent EUS-guided FNA and pathologic analysis.
  
  
  METHODS
  EUS images were obtained and correlated with cytologic findings after FNA. Texture features were extracted from the region of interest, and multifractal dimension vectors were introduced in the feature selection to the frame of the M-band wavelet transform. The sequential forward selection process was used for a better combination of features. By using the area under the receiver operating characteristic curve and other texture features based on separability criteria, a predictive model was built, trained, and validated according to the support vector machine theory.
  
  
  RESULTS
  From 67 frequently used texture features, 20 better features were selected, resulting in a classification accuracy of 99.07% after being added to 9 other features. A predictive model was then built and trained. After 50 random tests, the average accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of pancreatic cancer were 97.98 ± 1.23%, 94.32 ± 0.03%, 99.45 ± 0.01%, 98.65 ± 0.02%, and 97.77 ± 0.01%, respectively.
  
  
  CONCLUSIONS
  The limitations of this study include the small sample size and that the support vector machine was not performed in real time.
  
  
  CONCLUSIONS
  The classification of EUS images for differentiating pancreatic cancer from normal tissue by DIP is quite useful. Further refinements of such a model could increase the accuracy of EUS diagnosis of tumors.
authors.affiliation: Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China.
authors.forename: Min-Min
authors.initials: MM
authors.lastname: Zhang
authors.forename: Hua
authors.initials: H
authors.lastname: Yang
authors.forename: Zhen-Dong
authors.initials: ZD
authors.lastname: Jin
authors.forename: Jian-Guo
authors.initials: JG
authors.lastname: Yu
authors.forename: Zhe-Yuan
authors.initials: ZY
authors.lastname: Cai
authors.forename: Zhao-Shen
authors.initials: ZS
authors.lastname: Li
mesh_terms: D044963:Biopsy, Fine-Needle; D015331:Cohort Studies; D003937:Diagnosis, Differential; D019160:Endosonography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D010190:Pancreatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D050500:Pancreatitis, Chronic / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D058067:Wavelet Analysis