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.


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.
Zhang
Min-Min
MM
Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China.
Yang
Hua
H
Jin
Zhen-Dong
ZD
Yu
Jian-Guo
JG
Cai
Zhe-Yuan
ZY
Li
Zhao-Shen
ZS
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