Deep learning and automatic differentiation of pancreatic lesions in endoscopic ultrasound – a transatlantic study

Endoscopic ultrasound (EUS) allows characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include in mucinous (M-PCN) and non-mucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). While EUS is preferred for pancreatic lesion evaluation, its diagnostic accuracy is suboptimal. This multicentric study aims to develop a convolutional neural network (CNN) for detecting and distinguishing PCN (namely M-PCN and NM-PCN) and PSL (particularly P-DAC and P-NET). A CNN was developed with 378 EUS exams from 4 international reference centers (Centro Hospitalar Universitário São João, Hospital Universitario Puerta de Hierro Majadahonda, New York University Hospitals, Hospital das Clínicas FMUSP). 126.000 images were obtained – 19.528 M-PCN, 8.175 NM-PCN, 64.286 P-DAC, 29.153 P-NET and 4.858 normal pancreas images. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-DAC from P-NET. The total dataset was divided in a training and testing dataset (used for model’s evaluation) in a 90/10% ratio. The model was evaluated through its sensitivity, specificity, positive and negative predictive values and accuracy. The CNN had 99.1% accuracy for identifying normal pancreatic tissue, 99.0% and 99.8% for M-PCN and NM-PCN, respectively. P-DAC and P-NET were distinguished with 94.0% accuracy. Our group developed the first worldwide CNN capable of detecting and differentiating the commonest PCN and PSL in EUS images, using exams from 4 centers in two continents, minimizing the impact of the demographic bias. Larger multicentric studies are needed for technology implementation.