Multi-Modality Quantitiative Imaging for Evaluation of Response to Cancer Therapy

用于评估癌症治疗反应的多模态定量成像

• 批准号: 8334502
• 负责人: ERIC C. FREY
• 金额: $ 66.81万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-19 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8334502
• 关键词: Albumins; Anatomy; Angiogenesis Inhibitors; Animal Model; Biology; Brain Neoplasms; Cellularity; Clinical Trials; Cytotoxic Chemotherapy; Data; Environment; FDA approved; Glycolysis; Goals; Histology; Image; Image Analysis; Imaging Techniques; Immunotherapy; Individual; Knowledge; Lesion; Link; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Metabolic; Methionine; Methods; Metric; Modality; Modeling; Molecular Weight; NIH Program Announcements; Noise; Outcome; PET/CT scan; Pathway interactions; Patients; Permeability; Positron-Emission Tomography; Primary Brain Neoplasms; Principal Investigator; Process; Property; Protocols documentation; Reproducibility; Signal Transduction; Statistical Data Interpretation; Systemic disease; Technetium Tc 99m Pentetate; Therapeutic; Tracer; Tumor Biology; Tumor Volume; Validation; base; bevacizumab; cancer therapy; chemotherapeutic agent; imaging modality; improved; instrumentation; malignant breast neoplasm; radiotracer; response; simulation; single photon emission computed tomography; text searching; tool; treatment response; tumor; tumor growth

DESCRIPTION (provided by applicant): Cancers are heterogeneous in biology among patients, tumors in the same patient, and within tumors. As a result, they respond differently to therapy per patient, per tumor and within tumors. Different radiotracers and imaging modalities provide information about different aspects of biology and the physio-metabolic environments of the cancer. As a result, a single modality or radiotracer may not provide sufficient information to predict or assess response to therapy. We hypothesize that improved prediction and assessment of response can thus be obtained by combining quantitative image-derived parameters obtained from multiple imaging modalities or radiotracers. We propose to develop, optimize, and validate approaches for combining multiple image-derived parameters obtained from quantitative imaging procedures in order to optimally predict and assess treatment response. In particular, we propose to combine quantitative metrics from PET/CT, SPECT/CT, and MRI. We will first individually optimize the protocols, acquisition parameters, and imaging methods in order to get the most accurate and reliable parameters to combine. Optimally combining the parameters from different modalities requires knowledge of the reproducibility (precision) of the individual quantitative imaging parameters. We will thus use literature search, phantom studies, realistic simulations, and repeated patient studies to characterize the accuracy and precision of the individual quantitative imaging methods. We will then develop methods to combine the metrics to predict or assess treatment response per patient, per tumor and intra-tumor. We will apply and evaluate these methods in three clinical trials: dynamic and static FDG and FIT PET/CT to assess lung cancer response to cytotoxic chemotherapy; PET/CT and DCE- and DW-MRI in breast cancer response; and SPECT/CT, PET/CT and DCE- and DW-MRI to predict response of brain tumors to anti-angiogenic therapy. In these trials imaging parameters and their signatures will be linked to histology or survival outcomes to provide validation of the combined imaging parameter metrics.

描述（由申请人提供）：癌症在患者之间、同一患者的肿瘤之间以及肿瘤内部的生物学上是异质的。因此，它们对每个患者、每个肿瘤和肿瘤内的治疗的反应不同。不同的放射性示踪剂和成像方式提供了关于癌症的生物学和生理代谢环境的不同方面的信息。因此，单一模态或放射性示踪剂可能无法提供足够的信息来预测或评估对治疗的反应。我们假设，通过结合从多种成像方式或放射性示踪剂获得的定量图像衍生参数，可以改善对反应的预测和评估。我们建议开发，优化和验证方法，结合从定量成像程序获得的多个图像衍生参数，以最佳地预测和评估治疗反应。特别是，我们建议将来自PET/CT、SPECT/CT和MRI的定量度量联合收割机组合。我们将首先单独优化协议、采集参数和成像方法，以获得最准确和可靠的参数来进行联合收割机。最佳地组合来自不同模态的参数需要了解各个定量成像参数的再现性（精度）。因此，我们将使用文献检索、体模研究、真实模拟和重复患者研究来表征各个定量成像方法的准确度和精密度。然后，我们将开发方法来结合联合收割机的指标，以预测或评估每个患者，每个肿瘤和肿瘤内的治疗反应。我们将在三项临床试验中应用和评估这些方法：动态和静态FDG和FIT PET/CT，以评估肺癌对细胞毒性化疗的反应; PET/CT和DCE-和DW-MRI在乳腺癌反应中的作用; SPECT/CT，PET/CT和DCE-和DW-MRI预测脑肿瘤对抗血管生成治疗的反应。在这些试验中，成像参数及其特征将与组织学或生存结局相关，以验证组合成像参数指标。