A New Algorithm for Incidental Pancreatic Cyst Detection

Objectives: To develop an accurate, state-of-the-art algorithm for the incidental detection of pancreatic cystic lesions (PCLs) on computerized tomography (CT) and magnetic resonance imaging (MRI) scans. Methods: A SwinT-Unet-based architecture was developed for the incidental detection of PCLs. The algorithm was trained and validated on a robust dataset of retrospective CT and MRI studies collected from HT Medica centers located in eight different cities using scanners fabricated by four different manufacturers. Results: Our algorithm was able to detect 91.6% of the confirmed PCLs in the initial dataset with 91.6% sensitivity and 92.3% specificity, while 91.7% of the healthy controls were also correctly identified. Furthermore, our tool was remarkably capable of classifying these PCLs as mucinous or non-mucinous, determining their location within the pancreas with an accuracy of 88.5%, and identifying the presence of calcifications or scars within the PCLs with an accuracy of 96%. Conclusions: By integrating radiological data and state-of-the-art artificial intelligence techniques, we have developed an efficient tool for the incidental identification and initial characterization of PCLs, which present a substantial prevalence within the global population. Our algorithm facilitates early diagnosis of pancreatic abnormalities, which could have a profound impact on patient management and prognosis, particularly in the case of PCLs with malignant potential.