”Chakraborty et al. utilized radiomics features extracted from pre- surgical CT images, as markers for assessment of malignancy risk of BD- IPMNs. Similar to the previous studies, they categorized their cohort of 103 patients into low-risk and high-risk IPMNs based on final pathological findings after cyst resection. They extracted four new radiographically inspired features (enhanced boundary fraction, enhanced inside fraction, filled largest connected component fraction and average weighted eccentricity), along with intensity and orientation-based texture features from the CT images.”
Radiomics in stratification of pancreatic cystic lesions: Machine learning in action 
Vipin Dalala et al.
Cancer Letters,Volume 469,2020,Pages 228-237,

”This has led to an increased interest in radiomics, a high-throughput extraction of comprehensible data from standard of care images. Radiomics can be used as a diagnostic and prognostic tool in personalized medicine. It utilizes quantitative image analysis to extract features in conjunction with machine learning and artificial intelligence (AI) methods like support vector machines, random forest, and convolutional neural network for feature se- lection and classification. Selected features can then serve as imaging biomarkers to predict high-risk PCLs. Radiomics studies conducted heretofore on PCLs have shown promising results.”
Radiomics in stratification of pancreatic cystic lesions: Machine learning in action 
Vipin Dalala et al.
Cancer Letters,Volume 469,2020,Pages 228-237,

“Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score.”
Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography 
Leang Sim Nguon et al.
Diagnostics 2021, 11, 1052. https://doi.org/10.3390/diagnostics11061052

“Mucinous cystic neoplasms (MCN) of the pancreas are rare, low-grade tumors that occur predominantly in middle-aged women . They are reported to be maligant in about 6–27% of cases. Their most characteristic histopathological finding is the combination of mucin-producing epithelium supported by characteristic ovarian-like stroma that is not found in other pancreatic neoplasms. Furthermore, they usually are com- posed of large (> 2 cm) unilocular or multilocular macrocysts devoid of communication between the cyst and the pancreatic ductal system, and the presence of a fibrous capsule. All MCNs have the potential to transform into an invasive carcinoma, hence the necessity to resect them in their totality.” 
Mucinous cystic neoplasms of the pancreas: high-resolution cross-sectional imaging features with clinico-pathologic correlation 
Alejandro Garces-Descovich et al.
Abdom Radiol (2018) 43:1413–1422