Rad-path-omic tools for rectal cancer treatment evaluation

项目摘要：在2018年预计将新诊断为直肠癌的43,030名患者中， 大多数人将接受新辅助化疗(NAC)以减轻肿瘤负担。所有患者最终都会经历 一种积极的直肠切除术，其中25%表现出完全的病理反应(聚合酶链式反应，即疾病- NAC后的自由)在术后标本上。因此，这些患者受到了不必要的， 病态手术导致生活质量问题，在缺乏任何明确的、非侵入性的生物标志物的情况下 NAC在体内的反应。而多参数磁共振成像用于术前评估肿瘤反应和 回归到NAC，由于良性病变重叠出现，专家解释混乱且多变 治疗效果(如纤维化、溃疡)和肿瘤残留。 最近，通过放射组学对病变进行了更多的定量描述，涉及到高密度的 吞吐量，从成像中以计算机方式提取纹理或动态属性。肿瘤的放射学地图 环境可以基于它们的结构和功能特征来描述不同组织类型的存在， 视觉化为“低”和“高”特征表达的区域。事实上，NAC后的肿瘤环境 切除的直肠组织标本在不同的病理组织中显示出多样性和组织性。 类型，也与患者的预后和结果有关。然而，现有的放射组学方法仅尝试 描述组织区域的总体异质性，因为它们缺乏量化组织类型和 他们的组织做了NAC后的核磁共振检查。一种更全面、更准确的基于多基因的聚合酶链式反应预测器 因此，参数磁共振可以通过(A)量化结构和排列的密度和排列来构建 NAC后直肠磁共振成像的功能属性，以及(B)针对病理优化放射学描述符 通过与病理的空间相关性，验证NAC后MRI上的组织类型信息。 在这个提议中，我将开发新的放射组学工具，与空间上共同定位的“地面事实”相结合。 目的：通过NAC后磁共振成像，建立直肠癌患者聚合酶链式反应的预测指标。目标1将 开发和评估一种新的放射学描述符，以量化形态的空间组织(通过 NAC后病变的结构MRI)和生理学(通过对比增强功能MRI)的异质性 环境。目标2将专注于优化这一放射组织描述符以捕捉独特的组织 通过手术后病理信息到术前的空间映射，与聚合酶链式反应相关的组织 核磁共振检查。我的新描述符将通过机器学习预测器进行评估和验证，以识别患者 使用从两个不同机构获得的发现和坚持验证队列来展示聚合酶链式反应； 与临床反应指标进行比较。我的项目将建立在我的放射学有希望的初步结果上 组织描述符以及放射学-病理学联合注册框架，以产生临床可靠的 以及有效的基于放射组学的工具，可以实现直肠癌患者的个性化管理。

PROJECT SUMMARY: Of the estimated 43,030 patients who will be newly diagnosed with rectal cancer in 2018, a majority will receive neoadjuvant chemoradiation (NAC) to reduce tumor burden. All patients ultimately undergo an aggressive excision of the rectum, of which 25% exhibit complete pathologic response (pCR, i.e. disease- free after NAC) on the post-surgical specimen. These patients have therefore been subjected to an unnecessary, morbid procedure resulting in quality of life issues, in the absence of any definitive, non-invasive biomarkers for NAC response in vivo. While multi-parametric MRI is utilized to pre-operatively assess tumor response and regression to NAC, expert interpretation is confounded and variable due to overlapping appearance of benign treatment effects (e.g. fibrosis, ulceration) and residual tumor. Recently, more quantitative characterization of lesions has been enabled via radiomics, involving high- throughput, computerized extraction of textural or kinetic attributes from imaging. Radiomic maps of the tumor environment can depict presence of different tissue types based on their structural and functional characteristics, visualized as regions of “low” and ‘high” feature expression. In fact, the post-NAC tumor environment on the excised rectal tissue specimen has been shown to reflect a variety and organization in different pathologic tissue types, also linked to patient prognosis and outcome. However, existing radiomic approaches only attempt to characterize the overall heterogeneity in a tissue region, as they lack “ground truth” to quantify tissue types and their organization on post-NAC MRI. A more comprehensive and accurate predictor for pCR based off multi- parametric MRI could thus be constructed by (a) quantifying the density and arrangement of structural and functional attributes on post-NAC rectal MRIs, and (b) optimizing radiomic descriptors against pathologically validated information of post-NAC tissue types on MRI, via spatial correlation with pathology. In this proposal, I will develop novel radiomic tools in conjunction with spatially co-localized “ground truth” pathology to build a predictor for identifying rectal cancer patients exhibiting pCR via post-NAC MRI. Aim 1 will involve developing and evaluating a novel radiomic descriptor to quantify spatial organization of morphologic (via structural MRI) and physiologic (via contrast enhancement functional MRI) heterogeneity of the post-NAC lesion environment. Aim 2 will focus on optimizing this radiomic organization descriptor to capture distinctive tissue organization associated with pCR, via spatial mapping of post-surgical pathology information onto pre-operative MRI. My novel descriptor will be evaluated and validated via a machine learning predictor to identify patients exhibiting pCR using a discovery and a hold-out validation cohort, acquired from 2 different institutions; and compared with clinical markers of response. My project will build on promising preliminary results for my radiomic organization descriptor as well as a radiology-pathology co-registration framework, to result in a clinically reliable and impactful radiomics-based tool which could enable personalized management of rectal cancer patients.