Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

Abstract

The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC. Previous computer-aided developments have achieved certain accuracy for classification on segmented cystic lesions in CT. However, pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided framework for early differential diagnosis of pancreatic cysts without pre-segmenting the lesions using densely-connected convolutional networks (Dense-Net). The Dense-Net learns high-level features from whole abnormal pancreas and builds mappings between medical imaging appearance to different pathological types of pancreatic cysts. To enhance the clinical applicability, we integrate saliency maps in the framework to assist the physicians to understand the decision of the deep learning method. The test on a cohort of 206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has achieved an overall accuracy of 72.8%, which is significantly higher than the baseline accuracy of 48.1%, which strongly supports the clinical potential of our developed method.

Artificial intelligence for the management of pancreatic diseases

“…Additionally, saliency maps were generated to highlight the important pixels within the image and to visualize the critical areas that contributed to the classification output. The DL model achieved an accuracy of 73%, while the accuracy of the radiologists in this cohort was 48% 28 . Surprisingly, the saliency maps showed that critical information was derived not only from the region around the PCN, but also from the boundaries of the pancreas, indicating that the shape of the pancreas border contributes to the eventual decision.…”

“…The DL model achieved an accuracy of 73%, while the accuracy of the radiologists in this cohort was 48%. 28 Surprisingly, the saliency maps showed that critical information was derived not only from the region around the PCN, but also from the boundaries of the pancreas, indicating that the shape of the pancreas border contributes to the eventual decision. Wei et al developed a ML-based model to differentiate between SCNs and non-SCNs based on radiomic features from preoperative CT images.…”

“…Two studies developed algorithms to discriminate between four types of PCN on CT: intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN) and solid papillary neoplasm (SPN) 27,28 . The first study combined demographic variables with manually selected and CNN‐based imaging features.…”

“…25,26 Differentiation of pancreatic cystic lesions Two studies developed algorithms to discriminate between four types of PCN on CT: intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN) and solid papillary neoplasm (SPN). 27,28 The first study combined demographic variables with manually selected and CNN-based imaging features. The results showed that this model could differentiate between the types of PCN with an accuracy of 84%.…”

The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer

“…In addition to pancreatic cancer a number of groups have explored the application of AI for the identification of pancreatic cystic masses using traditional machine learning and demonstrated that it was possible to achieve average accuracies and AUC of 66% to 93% and 0.75 to 0.837. [37][38][39][40][41] Deep learning methods such as CNN have also been employed for PDAC detection and diagnosis. Liu et al [42] employed a faster region-based CNN and analysed more than 6000 CT images from 338 PDAC patients.…”