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

项目摘要 胰腺导管腺癌（PDAC）预计将成为癌症相关性疾病的第二大原因。 到2030年，人类将死亡，但没有准确的早期诊断测试。胰腺囊性病变 分支导管内乳头状黏液性肿瘤（IPMN）是最常见的前体肿瘤 胰腺癌的早期诊断几乎50%的流行囊肿是BD-IPMN。内镜超声（EUS）- PCL的引导细针穿刺（FNA）和囊液分析是标准治疗（SOC）诊断 方式。不幸的是，目前的SOC对于检测和风险来说是次优的（65-75%的准确度）。 BD-IPMN的分层[高度异型增生或腺癌（HGD-Ca）对低度异型增生（LGD）]。 BD-IPMN的手术目标是用HGD-Ca切除病变。然而，多个手术系列在 过去5年的研究表明，近一半至三分之二的切除BD-IPMNs仅存在LGD，通常 代表着过度治疗在这些情况下，手术切除的发病率（30%）和死亡率（2%） 的PCL是不合理的。另一方面，一些系列报告遗漏了（平均13%）浸润性癌症， 随访期间的BD-IPMN。目前还没有准确的检测BD-IPMN中HGD-Ca的方法。我们 利用了EUS引导针式共焦激光显微内镜（nCLE）的新型诊断方式， 一种提供PCL的体内实时光学活检的技术。在一项具有里程碑意义的研究中，我们证明了 nCLE引导的癌前病变（包括粘液性BD-IPMN）PCL诊断准确率高（97%）。我们 已经推导出HGD-Ca的nCLE特征，可以在BD中进行定性评估和定量分析， IPMN。我们还设计了一个基于CLE的卷积神经网络（CNN）-人工智能 (AI)BD-IPMN风险分层算法（HGD-Ca vs. LGD）。我们还开创了囊液下一代 测序（NGS）分析，增强BD-IPMN的诊断和风险分层。主 拟议研究的目的是准确的风险分层（HGD-Ca与LGD）BD-IPMN，以检测早期 PDAC和避免不合理的胰腺手术。在初步数据的支持下，我们的中心假设是， EUS-nCLE（手动和CNN-AI算法）以及EUS-nCLE与NGS和SOC变量的组合将 准确地对BD-IPMN进行风险分层。具体目标-（1）评估以下指标的准确性和观察者间一致性： 独立观察者中BD-IPMN的EUS-nCLE分化（HGD-Ca对LGD）。(2)改善和 前瞻性评价用于术前风险分层（HGD-Ca）的基于nCLE的CNN-AI算法的准确性 vs. BD-IPMN的LGD）。(3)评价包括nCLE、NGS和SOC在内的综合诊断方法， 提高BD-IPMN风险分层（HGD-Ca vs. LGD）的准确性。成功完成本 该项目的临床应用将为PCLs引起的PDAC的早期检测提供一种方法， 指导手术决策，以帮助避免不必要的切除或延误治疗。

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.