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的第二大病因。 到2030年死亡，但还没有准确的诊断测试来进行早期诊断。在胰腺囊性病变中 导管内乳头状黏液性肿瘤(IPMN)是最常见的前体 胰腺癌的病变。在所有流行的包囊中，近50%是BD-IPMN。内窥镜超声(EUS)- PCL的引导细针抽吸(FNA)和囊液分析是标准护理(SOC)诊断 医疗模式。不幸的是，当前的SOC对于检测和风险来说是次优的(65-75%的准确率 BD-IPMN的分层[高度不典型增生或腺癌(HGD-Ca)与低级别不典型增生(LGD)]。 BD-IPMNS手术的目标是切除伴有HGD-Ca的病变。然而，在过去的几年里，多个手术系列 过去5年的研究表明，近一半到三分之二切除的BD-IPMN仅有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，一些系列报道漏掉了(平均13%)侵袭性癌症 BD-IPMN随访期。目前还没有准确检测BD-IPMN中HGD-Ca的测试方法。我们 已经利用了EUS引导的基于针的共焦激光内窥镜(NCLE)的新的诊断模式， 一种提供PCL活体、实时、光学活组织检查的技术。在一项里程碑式的研究中，我们展示了 NCLE引导下诊断癌前病变(包括粘液性BD-IPMN)PCL的准确率高(97%)。我们 已推导出HGD-Ca的nCLE特征，可在BD-BD中进行定性评估和定量分析 IPMN。我们还设计了一个试点的基于CLE的卷积神经网络(CNN)-人工智能 (AI)算法对BD-IPMN进行风险分层(HGD-Ca与LGD)。我们还率先推出了囊液下一代 测序(NGS)分析，增强了对BD-IPMNS的诊断和风险分层。初级阶段 拟议研究的目的是准确地对(HGD-Ca与LGD)BD-IPMN进行风险分层以检测早期阶段 并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 准确对BD-IPMN进行风险分层。具体目标--(1)评估以下各项的准确性和观察者之间的一致性 BD-IPMN在独立观察者中的EUS-nCLE分化(HGD-Ca与LGD)。(二)完善和完善 前瞻性评估一种准确的基于nCLE的CNN-AI术前风险分层算法(HGD-CASE 对BD-IPMN的影响。(3)评估包括nCLE、NGS和SOC在内的综合诊断方法，以 提高BD-IPMNS风险分层(HGD-Ca与LGD)的准确性。成功完成这项工作 项目和在临床实践中的应用将提供一种早期检测PCLS引起的PDAC的方法， 指导手术决策，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.