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

内镜超声引导体内共聚焦激光内镜作为成像生物标志物，用于导管内乳头状粘液性肿瘤的准确风险分层

• 批准号: 10638754
• 负责人: Somashekar G. Krishna
• 金额: $ 39.98万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10638754
• 关键词: Abdomen; Adenocarcinoma; Agreement; Architecture; Artificial Intelligence; BRAF gene; Benign; Biopsy; Cancer Etiology; Cessation of life; Characteristics; Classification; Cohort Studies; Computer Models; Cyst; Cyst Fluid; Data; Decision Making; Detection; Diagnosis; Diagnostic; Diagnostic tests; Duct (organ) structure; Dysplasia; Early Diagnosis; Endoscopic Ultrasonography; Epithelium; Excision; Fine needle aspiration biopsy; Goals; Guidelines; High grade dysplasia; Histopathology; Image; International; KRAS2 gene; Knowledge; Lasers; Lesion; Malignant neoplasm of pancreas; Manuals; Methods; Modality; Morbidity - disease rate; Mucinous; Mucinous Neoplasm; Multicenter Studies; Mutation; Needles; Operative Surgical Procedures; Optical Biopsy; Pancreas; Pancreatectomy; Pancreatic Cyst; Pancreatic Ductal Adenocarcinoma; Pancreatic cystic neoplasia; Papillary; Reporting; Resected; Risk; Series; Technology; Testing; Time; Unnecessary Surgery; accurate diagnostics; artificial intelligence algorithm; artificial neural network; cancer invasiveness; clinical practice; convolutional neural network; current pandemic; design; detection test; diagnostic accuracy; diagnostic strategy; follow-up; imaging biomarker; imaging study; improved; in vivo; learning strategy; microendoscopy; model design; molecular marker; mortality; next generation sequencing; novel diagnostics; novel strategies; overtreatment; premalignant; progression risk; prospective; risk stratification; standard of care

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.

项目概要 胰腺导管腺癌（PDAC）预计将成为癌症相关的第二大原因 到 2030 年，胰腺囊性病变尚无准确的诊断测试来进行早期诊断。 (PCL)、分支导管 (BD) 导管内乳头状粘液性肿瘤 (IPMN) 是最常见的前兆 近 50% 的常见囊肿是 BD-IPMN 病变。 PCL 引导细针抽吸 (FNA) 和囊液分析是标准护理 (SOC) 诊断 不幸的是，当前的 SOC 对于检测和风险来说不是最佳的（65-75% 准确度）。 BD-IPMN 的分层[高度不典型增生或腺癌 (HGD-Ca) 与低度不典型增生 (LGD)]。 BD-IPMN 的手术目标是用 HGD-Ca 切除病变，然而，多次手术系列。 过去 5 年的研究表明，近一半到三分之二的切除 BD-IPMN 仅有 LGD，通常 在这些情况下，手术切除导致的发病率（30%）和死亡率（2%）。 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 目前尚无准确的检测方法来检测 BD-IPMN 中的 HGD-Ca。 采用了 EUS 引导针基共焦激光内窥镜 (nCLE) 的新型诊断方式， 一项提供 PCL 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，我们展示了一项技术。 nCLE 指导的癌前病变（包括粘液性 BD-IPMN）PCL 诊断准确率高（97%）。 导出了 HGD-Ca 的 nCLE 特征，可以在 BD- 中进行定性评估和定量分析 我们还设计了一个基于 CLE 的试点卷积神经网络 (CNN)——人工智能。 对 BD-IPMN 进行风险分层的 (AI) 算法（HGD-Ca 与 LGD）我们还开创了下一代囊液。 测序 (NGS) 分析，增强 BD-IPMN 的诊断和风险分层。 拟议研究的目标是准确对 BD-IPMN 进行风险分层（HGD-Ca 与 LGD），以检测早期阶段 PDAC 并避免不合理的胰腺手术 根据初步数据的支持，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) 评估准确性和观察者间的一致性。 独立观察者之间 BD-IPMN 的 EUS-nCLE 区分（HGD-Ca 与 LGD）(2) 改进和 前瞻性评估基于 nCLE 的准确 CNN-AI 算法，用于术前风险分层 (HGD-Ca 与 LGD）的 BD-IPMN (3) 评估包括 nCLE、NGS 和 SOC 的综合诊断方法。 提高 BD-IPMN 风险分层（HGD-Ca 与 LGD）的准确性 成功完成此任务。 该项目及其在临床实践中的应用将为PCL引起的PDAC的早期检测提供方法， 指导手术决策，帮助避免不必要的切除或延误治疗。