Computerized platform for interactive annotation and topological characterization of tumor associated vasculature for predicting response to immunotherapy in lung cancer

摘要：肿瘤微环境(TME)血管网络包含大量令人信服的解剖 以及嵌入在成像标尺上的生理信息。尽管放射组学等技术已经表明 在几种医学成像应用中，这种方法仅限于捕获属性 例如病变的形态和纹理，并且不能全面地表征或直观地描述 异常的TME血管系统。我们假设血管生成表现为特征性的拓扑和 结节周围血管构型的几何形态，与疾病进展和 结果。在这个项目中，我们建议在建筑中利用这些拓扑和几何构造 肿瘤相关血管系统的自适应分割、量化和可视化工具。为了证明 这些新工具在治疗反应评估中的临床疗效，我们建议以未满足的临床为目标 肺免疫治疗应答预测的需求。非小细胞肺癌(NSCLC)不到20% 接受免疫检查点抑制剂(ICIS)治疗的患者反应良好。此外，相关成本 是非常高的。评估肿瘤大小变化的分子标记和指标还不是很有效 在预测和监测对ICIS的反应方面。瘤内和瘤周的放射学特征最近被显示出来 在结果预测方面优于传统的生物标记物。然而，现有的标记都没有考虑到 肿瘤相关血管系统在TME临床评估中的作用，尽管有强有力的证据表明它在确定 疾病进展和对治疗的反应。一个关键的障碍是缺乏高效和易于使用的3- 供临床医生使用的三维(D)血管注释工具。尽管文献丰富，但要训练一辆自动驾驶汽车是很困难的 由于血管系统的高度异质性和复杂的三维形态，因此建立了分割模型。这是 尤其是在结节周围，病理血管表现异常但临床上表现异常。 相关的几何图形和拓扑结构。我们的目标是1)建立一个人在环路中的血管可视化和 基于拓扑主动学习的分割框架，2)描述图像的拓扑和几何特征 提取血管以获得一组新的血管放射组学标记，以及3)使用开发的 定量血管生物标记物建立预测非小细胞肺癌临床疗效的风险评分系统 接受ICI治疗的患者。具体地说，这些工具将进行优化，以确定哪些患者将受益 来自ICIS的治疗前CT。我们工作的一个主要优势是为临床医生提供直观的信息学 可视化异常血管系统的拓扑和几何属性的平台，从而使它们能够 从表型角度更好地理解血管结构在疾病进展中的作用。该队 将使用N=120名接受治疗的非小细胞肺癌患者的学习集来培训这些生物可解释的放射组学工具 在石溪大学医院接受ICI治疗。然后，开发的工具将在一组 N=300名患者，在大学医院克利夫兰医学中心接受治疗。

SUMMARY: The tumor microenvironment (TME) vascular network harbors a compelling amount of anatomical and physiological information embedded on the imaging scale. Although techniques like Radiomics have shown significant promise in several medical imaging applications, such approaches are limited to capturing properties such as lesion morphology and texture, and cannot comprehensively characterize or visualize the properties of the aberrant TME vasculature. We hypothesize that angiogenesis manifests as characteristic topological and geometrical patterns of vasculature in the nodule periphery, and is associated with disease progression and outcome. In this project, we propose to leverage these topological and geometrical constructs in building adaptive segmentation, quantification, and visualization tools for tumor associated vasculature. To demonstrate the clinical efficacy of these new tools in therapy response assessment, we propose to target unmet clinical needs in response prediction of lung immunotherapy. Fewer than 20% non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) respond favorably. Additionally, the associated costs are extremely high. Molecular markers and metrics evaluating changes in tumor size have not been very effective in predicting and monitoring response to ICIs. Intra- and peritumoral radiomic features have been recently shown to outperform traditional biomarkers in outcome prediction. None of the existing markers, however, consider the tumor associated vasculature in the clinical assessment of TME despite strong evidence of its role in determining disease progression and response to therapy. One critical obstacle is the lack of an efficient and easy-to-use 3- dimensional (D) vasculature annotation tool for clinicians. Despite rich literature, it is difficult to train an automatic segmentation model due of the highly heterogeneous and complex 3D morphology of vasculature. This is especially challenging near nodule periphery, where the pathological vasculature exhibits abnormal yet clinically relevant geometry and topology. We aim to 1) build a human-in-the-loop vasculature visualization and segmentation framework based on topological active learning, 2) characterize the topology and geometry of the extracted vessels to obtain a set of novel vascular radiomic markers, and 3) use the developed suite of quantitative vascular biomarkers to establish a risk scoring system for predicting clinical benefit for NSCLC patients undergoing ICI therapy. Specifically, these tools will be optimized to identify patients who will benefit from ICIs on pre-treatment CT. A major strength of our work is to provide clinicians an intuitive informatics platform to visualize topological and geometrical attributes of aberrant vasculature, thereby enabling them to better understand the role of vessel architecture in disease progression from a phenotypic perspective. The team will train these biologically interpretable radiomic tools using a learning set of N=120 NSCLC patients treated with ICI therapy at Stony Brook University Hospital. The developed tools will then be validated on a cohort of N=300 patients, treated at University Hospitals Cleveland Medical Center.