Background and purpose: The increased accumulation of hyaluronan correlates with cancer aggressiveness and chemoresistance. Evaluating hyaluronan content may provide information to individualized management in patients with pancreatic cancer. As an image analysis approach

texture analysis could predict tumor phenotype based on quantitative features in a non-invasive manner. This study aimed to investigate the value of CT texture analysis in evaluating hyaluronan content of pancreatic cancer. Methods: Patients with pancreatic ductal adenocarcinoma confirmed by pathology after surgery were retrospectively enrolled in Fudan University Shanghai Cancer Center from Jun. 2015 to Dec. 2015. All patients underwent CT examination before resection. According to hyaluronan staining in immunohistochemistry

patients were divided into high and low hyaluronan group. Clinical characteristics were compared between them. On portal venous CT images

region of interest (ROI) was manually segmented to encompass whole tumor by 3D-slicer. The pyradiomics package based on Python was used to extract 56 texture features. The inter-class correlation coefficient was used to assess the reproducibility of texture features. The Mann-Whitney U test was applied to select valuable features exhibiting significant difference between two groups. Stepwise logistic regression was applied to develop a model discriminating high/low hyaluronan tumor. The diagnostic power was assessed by receiver operating characteristic (ROC) curve analysis. Results: There were 13 patients in low hyaluronan group and 17 patients in high hyaluronan group. There was no significant difference in clinical characteristics between two groups. Four texture features (Volume

LeastAxis

Skewness

Energy) exhibited significant difference between two groups. Eventually

Skewness was selected into logistic regression by two-sided stepwise method. The area under curve of model was 0.738

with a specificity and sensitivity of 69.2% and 76.5% respectively. Conclusion: CT-based texture analysis is valuable in evaluating hyaluronan content of pancreatic cancer.