Endoscopic Ultrasound-guided In Vivo Confocal Laser Endomicroscopy as an Imaging Biomarker for the Accurate Risk Stratification of Intraductal Papillary Mucinous Neoplasms

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Project Summary Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-related death by 2030, yet there are no accurate diagnostic tests for early diagnosis. Among pancreatic cystic lesions (PCLs), branch duct (BD) intraductal papillary mucinous neoplasm (IPMN) is the most common precursor lesion for pancreatic cancer. Nearly 50% of all prevalent cysts are BD-IPMNs. Endoscopic ultrasound (EUS)- guided fine needle aspiration (FNA) of PCLs and cyst fluid analysis are standard-of-care (SOC) diagnostic modalities. Unfortunately, the current SOC is suboptimal (65-75% accuracy) for the detection and risk stratification [high-grade dysplasia or adenocarcinoma (HGD-Ca) vs. low-grade dysplasia (LGD)] of BD-IPMNs. The goal of surgery in BD-IPMNs is to resect lesions with HGD-Ca. However, multiple surgical series over the last 5 years have revealed that nearly half to two-thirds of resected BD-IPMNs had only LGD, often representing overtreatment. In these instances, the morbidity (30%) and mortality (2%) from surgical resection of PCLs are not justified. On the other hand, several series reports missed (mean 13%) invasive cancers in BD-IPMNs during follow-up. There are currently no accurate tests for detecting HGD-Ca in BD-IPMNs. We have utilized a novel diagnostic modality of EUS-guided needle-based confocal laser endomicroscopy (nCLE), a technology that provides in vivo, real-time, optical biopsies of PCLs. In a landmark study, we demonstrated a high accuracy (97%) for nCLE-guided diagnosis of precancerous (includes mucinous BD-IPMNs) PCLs. We have derived nCLE features of HGD-Ca that can be qualitatively assessed and quantitatively analyzed in BD- IPMNs. We also have designed a pilot CLE-based convolutional neural network (CNN)-artificial intelligence (AI) algorithm to risk-stratify BD-IPMNs (HGD-Ca vs. LGD). We have also pioneered cyst fluid Next-Generation Sequencing (NGS) analysis, augmenting the diagnosis and risk-stratification of BD-IPMNs. The primary objective of the proposed study is to accurately risk-stratify (HGD-Ca vs. LGD) BD-IPMNs to detect early-stage PDAC and avoid unjustified pancreatic surgery. Supported by preliminary data, our central hypothesis is that EUS-nCLE (manual and CNN-AI algorithm) and a combination of EUS-nCLE with NGS and SOC variables will accurately risk-stratify BD-IPMNs. Specific aims – (1) Evaluate the accuracy and interobserver agreement of EUS-nCLE differentiation (HGD-Ca vs. LGD) of BD-IPMNs among independent observers. (2) Improve and prospectively evaluate an accurate nCLE-based CNN-AI algorithm for presurgical risk stratification (HGD-Ca vs. LGD) of BD-IPMNs. (3) Evaluate an integrative diagnostic approach including nCLE, NGS, and SOC to improve the accuracy of risk stratification (HGD-Ca vs. LGD) of BD-IPMNs. Successful completion of this project and application in clinical practice will provide a method for early detection of PDAC arising from PCLs, guiding surgical decision-making to help avoid unwarranted resections or delayed treatment.