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.

肿瘤微环境（TME）血管网络包含大量的解剖学特征。 以及嵌入在成像标尺上的生理信息。尽管放射组学等技术表明 在几种医学成像应用中，这种方法被限制为捕获特性， 例如病变形态和纹理，并且不能全面地表征或可视化 异常的TME脉管系统我们假设血管生成表现为特征性的拓扑结构， 结节周围脉管系统的几何模式，与疾病进展相关， 结果。在这个项目中，我们建议利用这些拓扑和几何结构在建筑 用于肿瘤相关脉管系统的自适应分割、量化和可视化工具。证明 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 肺免疫治疗反应预测的需求。非小细胞肺癌（NSCLC） 用免疫检查点抑制剂（ICI）治疗的患者反应良好。此外，相关费用 非常高。评价肿瘤大小变化的分子标志物和指标不是很有效 预测和监测对ICI的反应。肿瘤内和肿瘤周围放射组学特征最近已被证明 在结果预测方面优于传统生物标志物。然而，现有的标记中没有一个考虑到 尽管有强有力的证据表明，肿瘤相关血管系统在TME的临床评估中起着决定性作用， 疾病进展和对治疗的反应。一个关键的障碍是缺乏一个有效和易于使用的3- 用于临床医生的三维（D）脉管系统注释工具。尽管有丰富的文献，但很难训练自动步枪。 由于脉管系统的高度异质性和复杂的3D形态，这是 尤其是在结节周边附近具有挑战性，其中病理性脉管系统表现出异常但临床上 相关的几何学和拓扑学。我们的目标是：1）建立一个人在环血管可视化， 基于拓扑主动学习的分割框架，2）表征 提取的血管，以获得一组新的血管放射性标记物，和3）使用开发的套件， 定量血管生物标志物，以建立预测NSCLC临床获益的风险评分系统 接受ICI治疗的患者。具体来说，这些工具将进行优化，以识别将受益的患者 治疗前CT上的脑缺血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 可视化异常脉管系统的拓扑和几何属性的平台，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。球队 将使用N=120例接受治疗的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 在斯托尼布鲁克大学医院接受ICI治疗。然后，将在一个队列中验证开发的工具， N=300例患者，在University Hospitals Cleveland Medical Center治疗。