J J Zhang1, Q Z Li2, J H Wang1, X Chen1, S Ren1, D D Ye1, H F Zhang1, Z Q Wang1. 1. Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210029, China. 2. Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, China.
Abstract
Objective: To explore the value of contrast-enhanced CT combined with texture analysis in differentiating pancreatic cancer from mass-forming pancreatitis in pancreatic head. Methods: A retrospective study collected 21 patients with pancreatic head mass-forming pancreatitis confirmed by surgery or biopsy and 47 patients with pancreatic ductal adenocarcinoma confirmed by surgery. The patients visited the Affiliated Hospital of Nanjing University of Chinese Medicine and the First Affiliated Hospital of Wannan Medical College between January 2014 and December 2017. Gender, age and CT findings were collected. The parenchymal phase was selected for texture analysis. The minimum absolute shrinkage and selection operator (LASSO) method was applied for dimensionality reduction.Two independent sample t-tests or Mann-Whitney U test were used for continuous variables based on the Shapiro-Wilks normality test results. Categorical variables were tested by Chi-square or Fisher test. By multivariable regression analysis, CT findings, CT texture analysis, CT findings combined with texture analysis prediction models were established. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of individual indicators and each prediction model. The Delong test was used to compare the area under the curve (AUC) of each model. Results: The CT findings prediction model consisted of CT value of lesion on pancreatic parenchymal phase and pancreatic duct penetrating sign. The texture analysis prediction model consists of root mean square and low grey level run emphasis_angle135. The AUC of them were not statistically different (Z=0.150,P>0.05). The combined predictive model had the better diagnostic performance (AUC 0.944, sensitivity 83.0%, specificity 95.2%, +LR 17.43, -LR 0.18) than CT sign prediction model (Z=2.008, P<0.05) and texture analysis prediction model(Z=2.236, P<0.05) were significantly different. Conclusions: The CT findings model and the texture analysis model have equivalent diagnostic performance in the differentiation of mass-forming pancreatitis and pancreatic cancer. The enhanced CT combined with texture analysis model has the best diagnostic efficiency and can further improve the diagnostic ability.
Objective: To explore the value of contrast-enhanced CT combined with texture analysis in differentiating pancreatic cancer from mass-forming pancreatitis in pancreatic head. Methods: A retrospective study collected 21 patients with pancreatic head mass-forming pancreatitis confirmed by surgery or biopsy and 47 patients with pancreatic ductal adenocarcinoma confirmed by surgery. The patients visited the Affiliated Hospital of Nanjing University of Chinese Medicine and the First Affiliated Hospital of Wannan Medical College between January 2014 and December 2017. Gender, age and CT findings were collected. The parenchymal phase was selected for texture analysis. The minimum absolute shrinkage and selection operator (LASSO) method was applied for dimensionality reduction.Two independent sample t-tests or Mann-Whitney U test were used for continuous variables based on the Shapiro-Wilks normality test results. Categorical variables were tested by Chi-square or Fisher test. By multivariable regression analysis, CT findings, CT texture analysis, CT findings combined with texture analysis prediction models were established. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of individual indicators and each prediction model. The Delong test was used to compare the area under the curve (AUC) of each model. Results: The CT findings prediction model consisted of CT value of lesion on pancreatic parenchymal phase and pancreatic duct penetrating sign. The texture analysis prediction model consists of root mean square and low grey level run emphasis_angle135. The AUC of them were not statistically different (Z=0.150,P>0.05). The combined predictive model had the better diagnostic performance (AUC 0.944, sensitivity 83.0%, specificity 95.2%, +LR 17.43, -LR 0.18) than CT sign prediction model (Z=2.008, P<0.05) and texture analysis prediction model(Z=2.236, P<0.05) were significantly different. Conclusions: The CT findings model and the texture analysis model have equivalent diagnostic performance in the differentiation of mass-forming pancreatitis and pancreatic cancer. The enhanced CT combined with texture analysis model has the best diagnostic efficiency and can further improve the diagnostic ability.