Operating characteristics in diagnosing pancreatic solid malignant lesions in the two groups (<i>n</i> = 163).

<p>CH-EUS, contrast-enhanced harmonic endoscopic ultrasonography; EUS, endoscopic ultrasonography group; AUC, area under receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value; 95% CI, 95% confidence interval.</p><p>*Suspicious cytology considered as diagnostic of malignancy.</p><p>#Atypical/suspicious cytology considered as a diagnostic of malignancy.</p><p>Operating characteristics in diagnosing pancreatic solid malignant lesions in the two groups (<i>n</i> = 163).</p

Representative example of a pancreatic neuroendocrine tumor with hyperenhancement.

<p>Conventional endoscopic ultrasonography (left) shows a hypoechoic mass with a clear margin at the pancreas body. Contrast-enhanced harmonic endoscopic ultrasonography (right) indicates that the mass has a hyperenhancement compared to the surrounding tissue.</p

Representative example of focal pancreatitis with hyperenhancement.

<p>Conventional endoscopic ultrasonography (left) shows a slightly hypoechoic area without a clear margin at the pancreas head. Contrast-enhanced harmonic endoscopic ultrasonography (right) indicates that enhancement in this area is higher than in the surrounding tissue, and a margin is clearly visible.</p

Characteristics of patients, tumors, and fine-needle aspiration in the two groups (<i>n</i> = 163).

<p>CH-EUS, contrast-enhanced harmonic endoscopic ultrasonography; EUS, endoscopic ultrasonography.</p><p>Characteristics of patients, tumors, and fine-needle aspiration in the two groups (<i>n</i> = 163).</p

Representative example of a pancreatic adenocarcinoma with hypoenhancement.

<p>Conventional endoscopic ultrasonography (left) shows a heterogeneous hypoechoic area without a clear margin at the pancreas head. Contrast-enhanced harmonic endoscopic ultrasonography (right) indicates that most of the area is hypovascular and the remaining area is hypervascular compared to the surrounding tissue. An irregular margin is visible.</p

Compared the three studies in results.

<p>SVM, support vector machine; ANN, artificial neural network. NP, normal pancreas;</p><p>CP, chronic pancreatitis; PC, pancreatic cancer; CCR, correct classification rate.</p

The processes of EUS image selection.

<p>As shown in the images of chronic pancreatitis: A1 shows an endoscopic ultrasound image of the head and body of the pancreas. Hyperechoic strands, parenchymal lobularity, hyperechoic foci, many hyperechoic dots with shadowing in the pancreatic parenchyma, and irregular pancreatic duct margins are identified. B1. Delineate the boundary around which contains more chronic pancreatitis features manually with a red circle as a region of interest (ROI). C1. Rectangular sub-images were extracted as large as they could from the ROIs to achieve uniformity of results easily. D1. the histogram was cut from the red circle for extraction of texture features. In the images of pancreatic cancer: A2. Select EUS images with solid pancreatic lesions which had been established by a positive cytology. B2.Delineate the boundary of each ultrasonographically identified lesion manually with a red circle as a region of interest (ROI) around the boundary of visible lesion. C2 and D2 were processed as C1 and D1.</p

Compared Support vector machine with artificial neural network.

<p>SVM, support vector machine; ANN, artificial neural network.</p

A sequential forward selection (SFS) algorithm was used to gain the best combination of features; the correct classification rate (CCR) for SVM was quantitative.

<p>We found CCR achieved the highest value when the features were added together to 16.</p

SFS algorithm.

<p>The horizontal axis represents the feature, and the vertical axis represents the possibility of inaccurate classification. The texture features identified using the distance between class algorithm were added one by one. The lowest error classification rate was observed when the first 16 features were added.</p