Imaging Based Dosimetry for Individualized Internal Emitter Therapy

描述(申请人提供)：用于个体化内发射体治疗的基于成像的剂量学摘要/摘要目前内发射体治疗的标准实践很少涉及治疗前计划，以优化肿瘤的辐射吸收剂量，同时避免关键器官(通常是骨髓)的毒性。这与计划外照射治疗形成鲜明对比，在外照射治疗计划中，肿瘤和周围器官的精确吸收剂量计算现在是强制性的。这样的治疗优化尚未被用于放射免疫治疗(RIT)等疗法，主要是因为准确的体内发射体剂量估计相对困难，而且临床研究迄今未能证明辐射吸收剂量与效果之间的一致关系。这些过去的研究通常依赖于次优的剂量估计方法，和/或没有考虑到预计也会影响结果的生物因素。长期目标是在临床上实施有效的个体化治疗计划，用于非霍奇金淋巴瘤(NHL)的放射性核素治疗，如I-131 Tositumomab RIT。本应用的目的是发展基于精确成像的肿瘤和骨髓剂量测定方法，并建立包含关键剂量学因素以及生物因素(如差异增殖、放射敏感性和对未标记抗体的敏感性)的肿瘤反应和骨髓毒性预测模型，这些因素将通过生物标记物研究确定。中心假设是，与通常使用的严格剂量测量方法相比，将剂量学和生物学相结合将能够更好地预测RIT结果。这一假设将在标准临床护理中接受I-131RIT治疗的难治性NHL患者和II期临床试验的一线患者中进行测试。为了实现这一应用的目标，将追求以下具体目标：1)开发利用CT图像信息的SPECT重建方法(不需要明确分割靶区边界)，以更准确地估计靶区内的三维活动分布，因为剂量不均匀影响治疗效果；2)开发基于成像的骨髓剂量学，将SPECT/CT与蒙特卡罗辐射传输相结合，并考虑定量CT确定的骨髓成分的变化；3)利用患者数据，基于剂量学因素和生物标志物研究中的生物因素(例如免疫组织化学/免疫分析中的Ki-67、P53和Flt3-L)，建立用于预测治疗结果(疗效、毒性)的多元回归模型；以及4)建立机制模型(对低剂量超放射敏感性进行修改和不进行修改)，以确定用于预测治疗结果的等效生物效应。这项拟议的工作具有创新性，因为与以往单纯关注剂量学的研究不同，这项工作将剂量学因素与生物因素相结合，以得出个性化治疗计划的最佳模型。这一贡献非常重要，因为一旦建立了治疗优化的方法和预测模型，医生将在未来使用它们来选择患者，并根据患者的不同定制RIT，以显著提高治疗的有效性。

DESCRIPTION (provided by applicant): Imaging based dosimetry for individualized internal emitter therapy Summary/Abstract The current standard practice for internal emitter therapy rarely involves pre-treatment planning to optimize the radiation absorbed dose to the tumor while avoiding critical organ (typically bone marrow) toxicity. This is in stark contrast to planning for external beam therapy where precise absorbed dose calculations to tumor and surrounding organs are now mandatory. Such therapy optimization has not been used in therapies such as radioimmunotherapy (RIT) primarily because accurate internal emitter dose estimation is relatively difficult and because clinical studies thus far have failed to show a consistent relationship between radiation absorbed dose and effect. These past studies typically relied on suboptimal methods of dose estimation and/or did not account for biologic factors that are also expected to affect outcome. The long-term goal is clinical implementation of effective individualized treatment planning for radionuclide therapies such as I-131 tositumomab RIT in non- Hodgkin's lymphoma (NHL). The objective in the present application is to develop accurate imaging based methods for tumor and bone marrow dosimetry and to develop predictive models for tumor response and bone marrow toxicity incorporating key dosimetric factors as well as biologic factors, such as differential proliferation, radiosensitivity and sensitivity to the unlabeled antibody, that will be determined by biomarker studies. The central hypothesis is that integrating dosimetry and biology will enable better prediction of RIT outcome than is obtained with the strictly dosimetric measures commonly used. The hypothesis will be tested in refractory NHL patients undergoing I-131 RIT in standard clinical care and in frontline patients on a phase II clinical trial. To accomplish the objective of this application the following specific aims will be pursued: 1) Develop SPECT reconstruction methods that utilize CT-image information (without needing explicit segmentation of target boundaries) to more accurately estimate the 3-D activity distribution in targets, because dose heterogeneity impacts the effect of the therapy; 2) Develop imaging based bone marrow dosimetry coupling SPECT/CT with Monte Carlo radiation transport and accounting for variations in marrow composition as determined by quantitative CT; 3) Using patient data develop a multivariate regression model for predicting therapy outcome (response, toxicity) based on dosimetric factors and biologic factors from biomarker studies (e.g., Ki-67, p53 and FLT3-L from immunohistochemistry/immunoassay); and 4) Develop a mechanistic model (with and without modification for low dose hyper-radiosensitivity) to determine the equivalent biologic effect for predicting therapy outcome. The proposed work is innovative because unlike past studies focusing purely on dosimetry this work combines dosimetric factors with biologic factors to arrive at the optimal model for individualized treatment planning. The contribution is highly significant because once the methodologies and predictive models for treatment optimization are established, they will be used in the future by physicians for patient selection and to tailor RIT on a patient-by-patient basis to considerably improve the efficacy of the treatment.