Dose-Response in Radionuclide Therapy

放射性核素治疗的剂量反应

• 批准号: 7588748
• 负责人: George Sgouros
• 金额: $ 39.76万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7588748
• 关键词: 3-Dimensional; Accounting; Address; Anatomic Models; Anatomy; Classification; Clinical; Clinical Trials Design; Collection; Complex; Computer software; Data; Data Analyses; Data Collection; Discipline of Nuclear Medicine; Doctor of Philosophy; Dose; Dose-Rate; Evaluation; FLT3 ligand; Hematopoietic; Ibritumomab Tiuxetan; Image; Malignant neoplasm of thyroid; Methodology; Modality; Modeling; Non-Hodgkin' s Lymphoma; Organ; Patients; Prior Therapy; Probability; Radioisotopes; Radionuclide therapy; Role; Spatial Distribution; Therapeutic; Thyroid Diseases; Toxic effect; United States National Institutes of Health; Variant; Y 90 Ibritumomab Tiuxetan; base; cancer therapy; dosimetry; experience; improved; iodine-131-tositumomab; response; tositumomab; tumor

Targeted radionuclide therapy is being actively investigated as a potential cancer therapy modality. The relationship between absorbed dose and tumor response or normal organ toxicity is important in optimizing radionuclide therapy. Current understanding of this relationship is almost completely derived from external beam rather than radionuclide therapy experience. Using data derived from clinical radionuclide therapy studies we propose to evaluate the potential role of radionuclide dosimetry in trial design and tumor response or toxicity prediction. The following questions will be addressed by this proposal: 1. What is the relationship between estimated absorbed dose and tumor and normal organ response? 2. Does patient specific, 3-D imaging-based dosimetry provide an advantage over a simpler, standard phantom-based approach? 3. Does radiobiologic modeling that accounts for differences in absorbed dose rate and uniformity improve response prediction? 4. How does prior therapy influence hematologic toxicity and the dose-response relationship? Using 3D-ID, a patient-specific 3-D dosimetry package developed by the PI with previous NIH support, the following aims are proposed to address these questions 1.Incorporate radiobiologic modeling in 3D-ID to utilize and interpret dose-rate and spatial uniformity information in evaluating response probability. 2.1.Obtain dose-response relationships in thyroid disease patients treated with 1-131. 2.2. Obtain dose-response relationships for non-hodgkins lymphoma patients treated with non- myeloablative Tositumomab (Bexxar; 131l-anti-CD20) and Ibritumomab Tiuxetan (Zevalin; 90Y-anti-CD20). 3. Compare dose-responserelationships obtained by accounting for dose-rate, non-uniformity and patient- specific anatomy (i.e., using 3D-ID) with those obtained using a simpler, standard-phantom based methodology (OLINDA); in the NHL studies, evaluate the role of FL.T3 ligand in improving thedose-response relationship for hematologic toxicity. Dosimetry has been assumed to be the best predictor of response following radionuclide treatment. Standardized, rigorous dosimetric analyses of radionuclide therapy data are needed to evaluate this assumption, identify the level of complexity required and to understand how other factors can impact the absorbed dose vs response relationship.

靶向放射性核素治疗作为一种潜在的癌症治疗方式正在被积极研究。这 吸收剂量与肿瘤反应或正常器官毒性之间的关系对于优化很重要 放射性核素治疗。目前对这种关系的理解几乎完全来源于外部 束而不是放射性核素治疗的经验。使用来自临床放射性核素治疗的数据 我们建议评估放射性核素剂量测定在试验设计和肿瘤中的潜在作用的研究 反应或毒性预测。本提案将解决以下问题： 1. 什么是 估计吸收剂量与肿瘤和正常器官反应之间的关系？ 2. 患者是否 基于特定 3D 成像的剂量测定比更简单、标准的基于体模的剂量测定具有优势 方法？ 3. 放射生物学模型是否可以解释吸收剂量率和辐射剂量率的差异？ 均匀性改善响应预测？ 4. 既往治疗如何影响血液学毒性以及 剂量反应关系？使用 3D-ID（由 PI 开发的患者特定 3D 剂量测定包） 在 NIH 先前的支持下，提出以下目标来解决这些问题 1.纳入 3D-ID 中的放射生物学模型可利用和解释剂量率和空间均匀性信息 评估响应概率。 2.1.获得接受治疗的甲状腺疾病患者的剂量-反应关系 与 1-131。 2.2.获得接受非霍奇金淋巴瘤患者治疗的剂量-反应关系 清髓性 Tositumomab（Bexxar；131l-抗 CD20）和 Ibritumomab Tiuxetan（Zevalin；90Y-抗 CD20）。 3. 比较通过考虑剂量率、不均匀性和患者剂量获得的剂量反应关系 特定的解剖结构（即使用 3D-ID）与使用更简单的基于标准模型获得的解剖结构 方法论（OLINDA）；在 NHL 研究中，评估 FL.T3 配体在改善剂量反应中的作用 与血液学毒性的关系。剂量测定被认为是反应的最佳预测指标 放射性核素治疗后。对放射性核素治疗数据进行标准化、严格的剂量分析 需要评估这个假设，确定所需的复杂程度并了解如何 其他因素也会影响吸收剂量与反应的关系。