Using Radiogenomics to Noninvasively Predict the Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas and Uncover Hidden Biology

项目摘要/摘要 每年大约有700,000个胰腺囊肿通过成像被偶然发现。其中高达70%的 放射学检测到的囊肿是导管内乳头状粘液性肿瘤(IPMN)，是真正的前体 胰腺癌是唯一一种5年相对生存率在个位数的实体恶性肿瘤。 一旦检测到，现有的成像方式和分子标记不能可靠地区分低/中 级别(良性)IPMN值得从需要手术的高级别/侵袭性(恶性)IPMN进行监测 手术切除，给临床带来很大挑战。根据我们小组产生的初步数据，我们 假设从术前提取的未探索类别的量化“放射”特征 计算机断层扫描(CT)将在预测恶性IPMN病理方面增加诊断价值， 与标准的放射学特征相比。我们进一步假设液体活组织检查可以测量 我们开发的血浆中循环的microRNA(一种miRNA基因组分类器(MGC))可能会有所帮助 以进一步提高诊断的准确性。本提案的目标是1)评估诊断 与标准放射学相比，新的放射学CT特征在预测IPMN病理中的表现 特点，使用回顾系列(目标1a)和预期多机构的数据和样本 一系列IPMN病例(目标1b)；2)生成原型临床决策模型(诺模图) 考虑到放射学数据、MGC和其他临床特征(目标2)；以及3)评估关系 IPMN肿瘤发生和/或发展的放射组学特征和生物学过程之间的关系 进步。通过利用跨学科的专业知识和我们机构独有的大部分现有数据，我们的 长期目标是发现一种结合定量成像和生物标记物的方法，这种方法是非侵入性的 在预测IPMN病理方面具有超出标准放射学特征的附加价值。 这一系列转化性研究具有培养临床可操作信息的潜力，这些信息可用于 快速、经济高效地为具有IPMN的个人提供个性化护理，并最终减轻 胰腺癌是一个主要的健康问题。

PROJECT SUMMARY/ABSTRACT Approximately 700,000 pancreatic cysts are incidentally detected by imaging each year. Up to 70% of these radiologically-detected cysts are intraductal papillary mucinous neoplasms (IPMNs), bona fide precursor lesions to pancreatic cancer, the only solid malignancy with a 5-year relative survival rate in the single digits. Once detected, existing imaging modalities and molecular markers cannot reliably distinguish low/moderate grade (benign) IPMNs that merit surveillance from high-grade/invasive (malignant) IPMNs that warrant surgical resection, posing a great clinical challenge. Based on preliminary data generated by our group, we hypothesize that unexplored categories of quantitative ‘radiomic’ features extracted from preoperative computed tomography (CT) scans will have added diagnostic value in predicting malignant IPMN pathology, compared to standard radiologic features. We further hypothesize that a liquid biopsy that measures microRNAs circulating in blood plasma (a miRNA genomic classifier (MGC)) that we have developed may help to further enhance diagnostic accuracy. The goals of this proposal are to 1) Evaluate the diagnostic performance of novel radiomic CT features in predicting IPMN pathology, compared to standard radiologic features, using data and specimens from a retrospective series (Aim 1a) and a prospective multi-institutional series of IPMN cases (Aim 1b); 2) Generate prototype clinical decision-making models (nomograms) that take into account radiomic data, the MGC, and other clinical characteristics (Aim 2); and 3) Evaluate the relationship between radiomic features and biological processes that underlie IPMN tumor development and/or progression. By leveraging interdisciplinary expertise and largely existing data unique to our institutions, our long-term goal is to discover a combined quantitative imaging and biomarker approach that is noninvasive and has added value in predicting IPMN pathology beyond that provided by standard radiologic characteristics. This line of translational research has potential to foster clinically actionable information that could be used to rapidly and cost-effectively personalize care for individuals with IPMNs and ultimately reduce the burden of pancreatic cancer as a major health problem.