Background: Differentiating pancreatic cancer (PC) from normal tissue by computer-aided diagnosis of EUS images were quite useful. The current study was designed to investigate the feasibility of using computer-aided diagnostic (CAD) techniques to extract EUS image parameters for the differential diagnosis of PC and chronic pancreatitis (CP). Methodology/Principal Findings: This study recruited 262 patients with PC and 126 patients with CP. Typical EUS images were selected from the sample sets. Texture features were extracted from the region of interest using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features; and, later, a support vector machine (SVM) predictive model was built, trained, and validated. Overall, 105 features of 9 categories were extracted from the EUS images for pattern classification. Of these features, the 16 were selected as a better combination of features. Then, SVM predictive model was built and trained. The total cases were randomly divided into a training set and a testing set. The training set was used to train the SVM, and the testing set was used to evaluate the performance of the SVM. After 200 trials of randomised experiments, the average accuracy, sensitivity, specificity, the positive and negative predictive values of pancreatic cancer were 94.2 +/- 0.1749%, 96.25 +/- 0.4460%, 93.38 +/- 0.2076%, 92.21 +/- 0.4249% and 96.68 +/- 0.1471%, respectively. Conclusions/Significance: Digital image processing and computer-aided EUS image differentiation technologies are highly accurate and non-invasive. This technology provides a kind of new and valuable diagnostic tool for the clinical determination of PC.
[1]Shanghai Jiao Tong Univ, Peoples Hosp Shanghai 1, Dept Gastroenterol, Shanghai 200030, Peoples R China.
[2]Second Mil Med Univ, Changhai Hosp, Dept Gastroenterol, Shanghai, Peoples R China.
[3]Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China.
[4]Second Mil Med Univ, Changhai Hosp, Dept Cardiothorac Surg, Shanghai, Peoples R China.
(8.0+极速模式)