Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy

Abstract

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.

Endoscopic Ultrasound-Guided Needle-Based Confocal Endomicroscopy as a Diagnostic Imaging Biomarker for Intraductal Papillary Mucinous Neoplasms

“…Additionally, the risk stratification of branch-duct IPMNs relying on a quantitative image interpretation of papillary structures is laborious and time-consuming. Therefore, incorporating artificial intelligence and machine learning to identify and measure key imaging features would be beneficial for enhancing both the accuracy and reliability of branch-duct IPMN risk stratification with nCLE imaging [ 25 ].…”

“…In recent years, there has been a growing focus on applying deep learning techniques to enhance the risk stratification of pancreatic cystic lesions. Mainly, convolutional neural networks, specialized neural networks designed for image-based tasks, have emerged as a prominent deep learning algorithm in this field [ 25 ]. In recent years, advancements in AI have been employed in detecting, classifying, and diagnosing pancreatic cysts.…”

“…AI empowers clinicians to delve into deeper layers of pancreatic cyst data that may not be discernible to humans. Integrating AI with novel diagnostic techniques like digital pathomics, radiomics, and genomics is paving the way for enhanced risk assessment of pancreatic cysts, offering promising possibilities for better understanding and stratification of IPMNs [ 25 , 31 ].…”

Molecular Pathology of Pancreatic Cystic Lesions with a Focus on Malignant Progression

“…When EUS is coupled with needle-based confocal laser endomicroscopy (nCLE), it can provide real-time imaging of the internal epithelium of pancreatic cysts and provide an excellent diagnosis of PCLs with an accuracy of over 90% [ 110 , 111 , 112 ]. In addition, nCLE evaluation of the “thickness” and “darkness” of the cyst epithelia allows the grading of dysplasia and thus risk stratification of IPMNs [ 113 ], which can be further augmented by machine learning/artificial intelligence (AI) [ 114 , 115 , 116 ]. In the analysis of high-yield (edited) EUS-nCLE videos using a preliminary AI model, a higher accuracy rate of 82% was achieved for detecting advanced neoplasia compared to the AGA guidelines (68.6%) and Fukuoka criteria (74.3%) [ 116 ].…”

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence

“…Identifying high-grade dysplasia or invasive carcinoma in IPMNs poses significant challenges owing to the detection accuracy of conventional methods such as EUS and cyst fluid analysis [ 46 ]. Given that most IPMNs are benign or exhibit low-grade dysplasia, thereby negating the need for surgical resection, CV models present a more efficient diagnostic alternative to traditional approaches that rely on confocal microscopy.…”