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

描述（由申请人提供）：患者之间、同一患者体内以及肿瘤内的肿瘤在生物学上是异质的。因此，每个患者、每个肿瘤以及肿瘤内部对治疗的反应都不同。不同的放射性示踪剂和成像方式提供有关癌症的生物学和生理代谢环境的不同方面的信息。因此，单一方式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模式或放射性示踪剂获得的定量图像衍生参数，可以改进反应的预测和评估。我们建议开发、优化和验证组合从定量成像程序获得的多个图像衍生参数的方法，以便最佳地预测和评估治疗反应。特别是，我们建议结合 PET/CT、SPECT/CT 和 MRI 的定量指标。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、最可靠的参数组合。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、模型研究、现实模拟和重复的患者研究来表征各个定量成像方法的准确性和精确度。然后，我们将开发方法来结合这些指标来预测或评估每个患者、每个肿瘤和肿瘤内的治疗反应。我们将在三个临床试验中应用和评估这些方法：动态和静态 FDG 和 FIT PET/CT 来评估肺癌对细胞毒性化疗的反应； PET/CT、DCE 和 DW-MRI 在乳腺癌反应中的作用； SPECT/CT、PET/CT 以及 DCE 和 DW-MRI 来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果相关联，以提供组合成像参数指标的验证。

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.