Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

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. Design A retrospective, controlled, single-center design was used. Setting The study took place at the Second Military Medical University, Shanghai, China. Patients There were 153 pancreatic cancer and 63 noncancer patients in this study. Intervention All patients underwent EUS-guided FNA and pathologic analysis. Main Outcome Measurements 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. Limitations The limitations of this study include the small sample size and that the support vector machine was not performed in real time. Conclusion 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. EUS can detect morphologic abnormalities of pancreatic cancer with high sensitivity but with limited specificity. 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. A retrospective, controlled, single-center design was used. The study took place at the Second Military Medical University, Shanghai, China. There were 153 pancreatic cancer and 63 noncancer patients in this study. All patients underwent EUS-guided FNA and pathologic analysis. 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. 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. The limitations of this study include the small sample size and that the support vector machine was not performed in real time. 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.