Centro Hospitalar Universitário São João Porto, Porto, Portugal
Miguel Mascarenhas Saraiva, MD, PhD1, António Miguel M P D. Martins Pinto da Costa, MD2, Francisco Mendes, MD1, Belen Agudo, MD3, Jessica Widmer, DO4, Uzma D. Siddiqui, MD, FACG5, Tiago Ribeiro, MD1, Miguel Martins, MD6, Pedro Cardoso, MD1, Joana Mota, MD1, Maria João Almeida, MD6, João Afonso, MD1, G. E. Kim, MD7, Daniel De la Iglesia Garcia, MD, PhD3, Ana Pérez González, MD3, Carlos Esteban Fernández-Zarza, MD3, Ana Garcia Garcia de Paredes, MD8, Maria Moris, MD9, Matheus Ferreira de Carvalho, MD10, Marcos Eduardo Lera dos Santos, MD10, Mariano Villaroel, MD11, Andrés Montoya, MD11, João Ferreira, PhD12, Khoon-Sheng Kok, MD13, Tamas Gonda, MD14, Joan-Berenguer Gornals, MD15, Masson Adams, MD16, Jack Di Palma, MD, MACG17, André Martins dos Santos, MD18, Filipe Vilas Boas, PhD6, Pedro Moutinho Ribeiro, MD, PhD6, Susana Lopes, MD, PhD6, Mariano Gonzalez Haba, MD3, Eduardo Hourneaux De Moura, MD, PhD19, Guilherme Macedo, MD, PhD1 1Centro Hospitalar Universitário São João, Porto, Porto, Portugal; 2Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Madrid, Spain; 3Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Madrid, Spain; 4NYU Langone Health, Mineola, NY; 5University of Chicago Medicine, Chicago, IL; 6Centro Hospitalar Universitário de São João, Porto, Porto, Portugal; 7Center for Endoscopic Research and Therapeutics (CERT), University of Chicago, Chicago, IL; 8Hospital Universitario Ramón y Cajal, Madrid, Madrid, Spain; 9Hospital Universitario Marqués de Valdecilla, GI, Santander, Spain,, Santander, Cantabria, Spain; 10Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo, São Paulo, Sao Paulo, Brazil; 11Hospital Britanico Buenos Aires, Buenos Aires, Argentina, Buenos Aires, Buenos Aires, Argentina; 12Faculdade de Engenharia da Universidade do Porto, Porto, Porto, Portugal; 13Royal Liverpool Hospital, Liverpool, UK, Liverpool, England, United Kingdom; 14NYU Langone Health, New York, NY; 15Hospital de Bellvitge, Barcelona, Catalonia, Spain; 16USA Health, University of South Alabama, Alabama, AL; 17USA Health, University of South Alabama, Mobile, AL; 18Faculdade de Medicina da Universidade do Porto, Porto, Porto, Portugal; 19Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Sao Paulo, Brazil Introduction: Endoscopic ultrasound (EUS) is the gold standard for evaluation and lesion sampling of pancreatic lesions. In this context, there is a need to differentiate each type of solid and cystic lesions. Additionally, in patients with a diagnosis of intraductal papillary mucinous neoplasms (IPMNs), the determination of grade of dysplasia is crucial for guiding clinical management. However, EUS diagnostic accuracy is still suboptimal. This multicenter study aimed to develop and validate convolutional neural networks (CNNs) to characterize solid and cystic pancreatic lesions and stratify IPMNs into high-grade dysplasia/carcinoma (HGD/C) and low-grade dysplasia (LGD). Methods: This multicenter study included EUS images from 14 centers in Portugal, Spain, the United Kingdom, Brazil, Argentina and the United States of America. Solid lesion differentiation (pancreatic ductal adenocarcinoma [PDAC] vs. pancreatic neuroendocrine tumor [PNET]) was achieved using 63795 images from 161 patients. A second CNN for distinction between mucinous and non-mucinous cystic lesions was trained on 202002 images from 110 patients. Finally, a third model used the subset of IPMNs (n=51046 frames, 30 patients) to distinguish between HGD/C and LGD. Histological confirmation was used as the gold standard. The datasets were split into training (70%), validation (20%), and testing (10%) subsets. Performance metrics included sensitivity, specificity, accuracy, and area under the precision-recall curve (AUC-PR). Results: For solid lesions, PDAC was identified with 98,9% sensitivity, 90.0% specificity and 95.8% accuracy, while PNET was detected with 97.8% sensitivity, 97.1% specificity and 97.4% accuracy. Mucinous cystic lesions were identified with 98.5% sensitivity, 88.6% specificity and 95.7% accuracy, while non-mucinous cystic lesions were detected with 98.4% sensitivity, 98.7% specificity and 98.6% accuracy. The model distinguished IPMNs with HGD/C from those with LGD with 97.5% sensitivity, 94.3% specificity and 89.8% accuracy. Discussion: This is the first multicenter and interoperable study showcasing a holistic approach to focal pancreatic lesions, identifying those with higher malignant potential. To our knowledge this is the first model capable of stratifying IPMNs into LGD and HGD/C, potentially reducing unnecessary surgeries and ensuring timely intervention for high-risk patients. Real-time implementation of these solutions could significantly improve decision-making in EUS and optimize patient outcomes.
Figure: Study Design Overview
Figure: Example of CNN detection and clasification tasks
Disclosures: Miguel Mascarenhas Saraiva indicated no relevant financial relationships. António Miguel Martins Pinto da Costa indicated no relevant financial relationships. Francisco Mendes indicated no relevant financial relationships. Belen Agudo indicated no relevant financial relationships. Jessica Widmer indicated no relevant financial relationships. Uzma Siddiqui: Boston Scientific – Consultant, Grant/Research Support, Speakers Bureau. ConMed – Consultant, Speakers Bureau. Cook – Consultant, Speakers Bureau. Medtronic – Consultant, Speakers Bureau. Olympus – Consultant, Grant/Research Support, Speakers Bureau. Tiago Ribeiro indicated no relevant financial relationships. Miguel Martins indicated no relevant financial relationships. Pedro Cardoso indicated no relevant financial relationships. Joana Mota indicated no relevant financial relationships. Maria João Almeida indicated no relevant financial relationships. João Afonso indicated no relevant financial relationships. G. E. Kim indicated no relevant financial relationships. Daniel De la Iglesia Garcia indicated no relevant financial relationships. Ana Pérez González indicated no relevant financial relationships. Carlos Esteban Fernández-Zarza indicated no relevant financial relationships. Ana Garcia Garcia de Paredes indicated no relevant financial relationships. Maria Moris indicated no relevant financial relationships. Matheus Ferreira de Carvalho indicated no relevant financial relationships. Marcos Eduardo Lera dos Santos indicated no relevant financial relationships. Mariano Villaroel indicated no relevant financial relationships. Andrés Montoya indicated no relevant financial relationships. João Ferreira indicated no relevant financial relationships. Khoon-Sheng Kok indicated no relevant financial relationships. Tamas Gonda indicated no relevant financial relationships. Joan-Berenguer Gornals indicated no relevant financial relationships. Masson Adams indicated no relevant financial relationships. Jack Di Palma indicated no relevant financial relationships. André Martins dos Santos indicated no relevant financial relationships. Filipe Vilas Boas indicated no relevant financial relationships. Pedro Moutinho Ribeiro indicated no relevant financial relationships. Susana Lopes indicated no relevant financial relationships. Mariano Gonzalez Haba indicated no relevant financial relationships. Eduardo Hourneaux De Moura indicated no relevant financial relationships. Guilherme Macedo indicated no relevant financial relationships.
Miguel Mascarenhas Saraiva, MD, PhD1, António Miguel M P D. Martins Pinto da Costa, MD2, Francisco Mendes, MD1, Belen Agudo, MD3, Jessica Widmer, DO4, Uzma D. Siddiqui, MD, FACG5, Tiago Ribeiro, MD1, Miguel Martins, MD6, Pedro Cardoso, MD1, Joana Mota, MD1, Maria João Almeida, MD6, João Afonso, MD1, G. E. Kim, MD7, Daniel De la Iglesia Garcia, MD, PhD3, Ana Pérez González, MD3, Carlos Esteban Fernández-Zarza, MD3, Ana Garcia Garcia de Paredes, MD8, Maria Moris, MD9, Matheus Ferreira de Carvalho, MD10, Marcos Eduardo Lera dos Santos, MD10, Mariano Villaroel, MD11, Andrés Montoya, MD11, João Ferreira, PhD12, Khoon-Sheng Kok, MD13, Tamas Gonda, MD14, Joan-Berenguer Gornals, MD15, Masson Adams, MD16, Jack Di Palma, MD, MACG17, André Martins dos Santos, MD18, Filipe Vilas Boas, PhD6, Pedro Moutinho Ribeiro, MD, PhD6, Susana Lopes, MD, PhD6, Mariano Gonzalez Haba, MD3, Eduardo Hourneaux De Moura, MD, PhD19, Guilherme Macedo, MD, PhD1. P4316 - Artificial Intelligence for Comprehensive Pancreatic Lesion Profiling: Multicenter Validation of Solid/Cystic Lesion Detection/Characterization and IPMN Grading, ACG 2025 Annual Scientific Meeting Abstracts. Phoenix, AZ: American College of Gastroenterology.
