Imaging Based Dosimetry for Individualized Internal Emitter Therapy

描述（由申请人提供）：用于个体化内部发射器治疗的基于成像的剂量测定总结/摘要当前用于内部发射器治疗的标准实践很少涉及预治疗规划，以优化肿瘤的辐射吸收剂量，同时避免关键器官（通常为骨髓）毒性。这与外部射束治疗的计划形成鲜明对比，在外部射束治疗中，现在必须精确计算肿瘤和周围器官的吸收剂量。这种治疗优化尚未用于诸如放射免疫治疗（RIT）的治疗中，主要是因为准确的内部发射器剂量估计相对困难，并且因为迄今为止的临床研究未能显示出辐射吸收剂量与效应之间的一致关系。这些过去的研究通常依赖于次优剂量估计方法和/或未考虑预期也会影响结局的生物学因素。长期目标是在非霍奇金淋巴瘤（NHL）中临床实施放射性核素治疗（如I-131托西莫单抗RIT）的有效个体化治疗计划。本申请的目的是开发用于肿瘤和骨髓剂量测定的基于精确成像的方法，并开发用于肿瘤反应和骨髓毒性的预测模型，其结合关键剂量测定因素以及生物因素，例如差异增殖、放射敏感性和对未标记抗体的敏感性，其将通过生物标志物研究确定。中心假设是，结合剂量学和生物学将能够更好地预测RIT的结果比得到严格的剂量测量常用的。该假设将在标准临床治疗中接受I-131 RIT的难治性NHL患者和II期临床试验的一线患者中进行检验。为了实现本申请的目的，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（不需要目标边界的明确分割）以更准确地估计目标中的3D活性分布，因为剂量异质性影响治疗的效果; 2）开发基于成像的骨髓剂量测定，将SPECT/CT与蒙特卡罗辐射传输耦合，并解释由定量CT确定的骨髓组成的变化; 3）使用患者数据开发多变量回归模型，用于基于来自生物标志物研究的剂量测定因子和生物因子（例如，Ki-67、p53和FLT 3-L，来自免疫组织化学/免疫测定）;和4）开发机制模型（针对低剂量超放射敏感性进行和不进行修改），以确定用于预测治疗结果的等效生物学效应。拟议的工作是创新的，因为不像过去的研究，专注于剂量学，这项工作结合了剂量学因素与生物因素，以达到个性化治疗计划的最佳模式。这一贡献是非常重要的，因为一旦建立了治疗优化的方法和预测模型，它们将在未来被医生用于患者选择，并在逐个患者的基础上定制RIT，以大大提高治疗的疗效。 公共卫生相关性：拟议的研究与公共卫生有关，因为它有望为临床医生提供方法和预测模型，这些模型对基于准确的治疗前剂量计算和广泛可用的生物标志物结果的未来放射免疫治疗（RIT）治疗优化至关重要。RIT的疗效很可能会大大提高这种个性化的计划，因此，拟议的研究是相关的NIH的使命的一部分，涉及到发展的基础知识和知识的应用，以延长健康的生活，减少疾病和残疾的负担。

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. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to public health because it is expected to provide the clinicians with the methodology and predictive models essential for future radioimmunotherapy (RIT) treatment optimization based on accurate pre-therapy dosimetric calculations and results from widely available biomarkers. The efficacy of RIT is likely to improve substantially with such individualized planning, hence the proposed research is relevant to the part of NIH's mission that pertains to developing fundamental knowledge and the application of that knowledge to extend healthy life and reduce the burdens of illness and disability.