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

用于肿瘤相关脉管系统的交互式注释和拓扑表征的计算机化平台，用于预测肺癌免疫治疗的反应

• 批准号: 10424637
• 负责人: Chao Chen
• 金额: $ 21.74万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10424637
• 关键词: 3-Dimensional; Active Learning; Anatomy; Architecture; Attention; Awareness; Biological Markers; Biomechanics; Blood Vessels; Cancer Patient; Characteristics; Clinical; Clinical assessments; Complex; Data Set; Disease; Disease Outcome; Disease Progression; Exhibits; Geometry; Goals; Grain; Growth; Image; Immune checkpoint inhibitor; Immunologic Markers; Immunotherapy; Informatics; Intuition; Learning; Lesion; Lesion by Morphology; Literature; Location; Lung; Lung CAT Scan; Lung Neoplasms; Malignant neoplasm of lung; Mathematics; Measurement; Medical Imaging; Medical center; Methods; Modeling; Monitor; Morphology; Neoadjuvant Therapy; Nodule; Non-Small-Cell Lung Carcinoma; Outcome; Pathologic; Patients; Pattern; Phenotype; Physiological; Play; Property; Risk; Role; Shapes; Structure; System; Techniques; Testing; Texture; Training; Tumor-Associated Vasculature; University Hospitals; Visualization; Visualization software; Work; X-Ray Computed Tomography; angiogenesis; annotation system; artificial intelligence algorithm; automated segmentation; base; checkpoint therapy; chest computed tomography; clinical efficacy; clinical predictors; clinically relevant; cohort; computerized; cost; differential geometry; human-in-the-loop; imaging Segmentation; imaging biomarker; improved; informatics tool; innovation; lung visualization; machine learning framework; malignant breast neoplasm; molecular marker; novel; outcome prediction; predicting response; predictive marker; programmed cell death ligand 1; radiomics; responders and non-responders; response; success; tool; treatment response; tumor; tumor behavior; tumor microenvironment

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.

摘要：肿瘤微环境 (TME) 血管网络拥有大量解剖学特征 以及嵌入成像秤上的生理信息。尽管放射组学等技术已经表明 在多种医学成像应用中具有重大前景，但此类方法仅限于捕获属性 例如病变形态和纹理，并且不能全面表征或可视化其属性 异常的 TME 脉管系统。我们假设血管生成表现为特征性拓扑和 结节周围脉管系统的几何图案，与疾病进展和 结果。在这个项目中，我们建议利用这些拓扑和几何结构来构建 用于肿瘤相关脉管系统的自适应分割、量化和可视化工具。展示 这些新工具在治疗反应评估中的临床疗效，我们建议针对未满足的临床 肺部免疫治疗反应预测的需求。非小细胞肺癌 (NSCLC) 率低于 20% 使用免疫检查点抑制剂（ICIs）治疗的患者反应良好。此外，相关费用 非常高。评估肿瘤大小变化的分子标记和指标并不是很有效 预测和监测对 ICI 的反应。最近显示了瘤内和瘤周放射组学特征 在结果预测方面优于传统生物标志物。然而，现有的标记都没有考虑 尽管有强有力的证据表明肿瘤相关脉管系统在 TME 的临床评估中具有决定作用 疾病进展和对治疗的反应。一个关键障碍是缺乏一种高效且易于使用的 3- 为临床医生提供的维度 (D) 脉管系统注释工具。尽管文献丰富，但训练自动机器仍然很困难 由于脉管系统的高度异质性和复杂的 3D 形态，分割模型。这是 在结节周边附近尤其具有挑战性，那里的病理脉管系统在临床上表现出异常 相关的几何和拓扑。我们的目标是 1) 构建人机循环脉管系统可视化 基于拓扑主动学习的分割框架，2）表征拓扑和几何形状 提取血管以获得一组新型血管放射组学标记，并且3）使用开发的套件 定量血管生物标志物建立风险评分系统来预测 NSCLC 的临床获益 接受 ICI 治疗的患者。具体来说，这些工具将进行优化，以确定受益的患者 来自治疗前 CT 上的 ICI。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 平台可视化异常脉管系统的拓扑和几何属性，从而使他们能够 从表型的角度更好地理解血管结构在疾病进展中的作用。团队 将使用 N=120 名接受治疗的 NSCLC 患者的学习集来训练这些生物学上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人身上进行验证 N=300 名患者，在克利夫兰大学医院医学中心接受治疗。