Rad-path-omic tools for rectal cancer treatment evaluation

项目总结：在2018年新诊断出直肠癌的估计43，030例患者中， 大多数人将接受新辅助化疗（NAC）以减轻肿瘤负担。所有患者最终都会接受 直肠的侵袭性切除，其中25%表现出完全的病理反应（pCR，即疾病- NAC后游离）。因此，这些病人受到了不必要的， 导致生活质量问题的病态手术，在缺乏任何明确的非侵入性生物标志物的情况下， 体内NAC反应。虽然多参数MRI用于术前评估肿瘤反应， 回归至NAC，由于良性肿瘤的重叠表现，专家解释混淆且可变 治疗效果（例如纤维化、溃疡）和残留肿瘤。 最近，通过放射组学已经能够对病变进行更多的定量表征， 生产量，从成像中计算机化提取纹理或动力学属性。肿瘤的放射组学图谱 环境可以基于不同组织类型的结构和功能特征来描绘它们的存在， 可视化为“低”和“高”特征表达的区域。事实上，NAC治疗后的肿瘤环境 切除的直肠组织标本显示出不同病理组织的多样性和组织性 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试图 表征组织区域中的整体异质性，因为它们缺乏量化组织类型的“地面实况”， 他们的组织在NAC后的核磁共振成像。一种更全面、更准确的pCR预测器， 因此，参数MRI可以通过以下方式构建：（a）量化结构和组织的密度和排列， NAC后直肠MRI的功能属性，以及（B）优化放射组学描述符， 通过与病理学的空间相关性，在MRI上验证NAC后组织类型的信息。 在这个建议中，我将开发新的放射性工具，结合空间共定位的“地面实况”， 病理学来构建用于通过NAC后MRI鉴定表现出pCR的直肠癌患者的预测因子。目标1将 涉及开发和评估新放射组学描述符以量化形态学（通过 NAC后病变的结构MRI）和生理（通过对比增强功能MRI）异质性 环境AIM 2将专注于优化这种放射组织描述符，以捕获独特的组织 通过将术后病理学信息空间映射到术前， 核磁共振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2个不同机构获得的发现和保留验证队列展示pCR;以及 与临床反应标志物相比。我的项目将建立在我的放射组学的有希望的初步结果的基础上。 组织描述符以及放射学-病理学共配准框架，以产生临床上可靠的 和基于放射学的有效工具，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.