To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.

In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.

Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.

Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.

Pancreatic neoplasms; Diagnosis; Faster R-CNN; Clinical application