Quantitative PET/CT Analysis to Improve Evaluation of Tumor Response

定量 PET/CT 分析可改善肿瘤反应评估

• 批准号: 8683134
• 负责人: Wei Lu
• 金额: $ 30.9万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8683134
• 关键词: Algorithms; Anatomy; Caliber; Cancer Patient; Characteristics; Clinical; Clinical Trials; Complement; Diagnostic; Diagnostic tests; Esophageal Neoplasms; Evaluation; Failure; Future; Goals; Health Sciences; Image; Image Analysis; Institution; Logistic Regressions; Machine Learning; Malignant Neoplasms; Malignant neoplasm of esophagus; Manuals; Maryland; Measures; Metabolic; Methods; Modality; Modeling; Morbidity - disease rate; Operative Surgical Procedures; Oregon; Outcome; Pathologic; Patients; Physicians; Positron-Emission Tomography; Property; Quality of life; Receiver Operating Characteristics; Relative (related person); Reporting; Sensitivity and Specificity; Site; Spatial Distribution; Specificity; Staging; Survival Rate; Techniques; Testing; Texture; Universities; Validation; Variant; Work; X-Ray Computed Tomography; base; chemoradiation; clinical practice; cost; demographics; forest; image registration; improved; mortality; novel; predictive modeling; prospective; public health relevance; response; tool; treatment strategy; tumor; uptake

DESCRIPTION (provided by applicant): We propose to develop and validate a quantitative Positron Emission Tomography / Computed Tomography (PET/CT) image analysis framework to improve the evaluation of esophageal tumor response to chemoradiotherapy (CRT) in patients with locally advanced esophageal cancer. In Aim 1, we will extract comprehensive spatial and temporal features of a tumor from PET/CT images and evaluate their ability in predicting tumor response to CRT. These features will quantify the spatial characteristics of a tumor along with their changes due to CRT, adding a great amount of information to the current non-volumetric PET/CT response measures. Also, we will use image registration techniques to align pre-CRT images with post-CRT images, making it possible to quantify the spatial changes at the original tumor site. In Aim 2, we will construct and test reliable predictive models of tumo response to CRT using machine learning techniques with spatial- temporal PET/CT features, clinical parameters and demographics as input. The models will identify an optimal subset of predictive features and utilize PET and CT information in chorus. In Aim 3, we will develop a novel multi-modality adaptive region-growing algorithm for tumor delineation in PET/CT. We will compare the accuracy and precision of the resulting predictive models against those in which tumor is delineated using conventional methods (manual contouring or thresholding). This comparison will help us understand to what degree the prediction of tumor response depends on the tumor delineation methods. Finally, we will use pathologic response and survival as the end points and ground truth to cross-validate each predictive model. If all aims are achieved, the proposed PET/CT image analysis framework may provide a highly accurate diagnostic tool. This, will complement other diagnostic tests in assisting physicians in making a treatment decision to more appropriately select patients for surgery, thus avoiding the mortality and morbidity of surgery in responders for whom surgery can be safely deferred; while improving local control and survival in non- responders for whom surgery should be considered. Therefore, this work has the potential to improve outcomes by safely deferring surgery which will improve our locally advanced esophageal cancer patient's quality of life while simultaneously reducing costs.

描述(由申请人提供)：我们建议开发和验证一种定量正电子发射断层扫描/计算机断层扫描(PET/CT)图像分析框架，以改善局部晚期食道癌患者对化疗(CRT)的食道肿瘤反应的评估。在目标1中，我们将从PET/CT图像中提取肿瘤的综合空间和时间特征，并评估其预测肿瘤对CRT的反应的能力。这些特征将量化肿瘤的空间特征以及它们因CRT而发生的变化，为目前的非体积PET/CT反应措施增加了大量信息。此外，我们将使用图像配准技术将CRT前图像与CRT后图像对准，使量化原始肿瘤部位的空间变化成为可能。在目标2中，我们将使用机器学习技术，以PET/CT的时空特征、临床参数和人口统计学为输入，构建和检验肿瘤对CRT反应的可靠预测模型。这些模型将确定预测特征的最佳子集，并同时利用正电子发射计算机断层扫描和CT信息。在目标3中，我们将开发一种新的多模式自适应区域生长算法来在PET/CT中勾画肿瘤。我们将把预测模型的精确度和精确度与用传统方法(人工勾画或阈值)勾画肿瘤的模型进行比较。这种比较将有助于我们了解肿瘤反应的预测在多大程度上依赖于肿瘤的描绘方法。最后，我们将使用病理反应和存活率作为终点和基本事实来交叉验证每个预测模型。如果所有目标都达到了，所提出的PET/CT图像分析框架可能会提供一种高度准确的诊断工具。这将补充其他诊断测试，帮助医生做出治疗决定，更适当地选择患者进行手术，从而避免可安全推迟手术的应答者的手术死亡率和发病率；同时改善应考虑手术的无应答者的局部控制和存活率。因此，这项工作有可能通过安全地推迟手术来改善预后，这将在降低成本的同时提高我们当地晚期食道癌患者的生活质量。