Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy
Joanna Jiang,
Wei-Lun Chao,
Troy Cao,
Stacey Culp,
Bertrand Napoléon,
Samer El-Dika,
Jorge D. Machicado,
Rahul Pannala,
Shaffer Mok,
Anjuli K. Luthra,
Venkata S. Akshintala,
Thiruvengadam Muniraj,
Somashekar G. Krishna
Affiliations
Joanna Jiang
Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
Wei-Lun Chao
Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
Troy Cao
College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Stacey Culp
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Bertrand Napoléon
Department of Gastroenterology, Jean Mermoz Private Hospital, 69008 Lyon, France
Samer El-Dika
Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA 94305, USA
Jorge D. Machicado
Division of Gastroenterology, University of Michigan, Ann Arbor, MI 48109, USA
Rahul Pannala
Division of Gastroenterology and Hepatology, Mayo Clinic Arizona, Phoenix, AZ 85054, USA
Shaffer Mok
Division of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA
Anjuli K. Luthra
Division of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA
Venkata S. Akshintala
Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA
Thiruvengadam Muniraj
Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06510, USA
Somashekar G. Krishna
Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.
