Imaging and Dosimetry of Yttrium-90 for Personalized Cancer Treatment

用于个性化癌症治疗的 Yttrium-90 成像和剂量测定

基本信息

批准号：
10669186
负责人：
YUNI K DEWARAJA
金额：
$ 68.43万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-15 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10669186
关键词：
90Y Acceleration Address Adoption Cancer Etiology Cessation of life Clinic Clinical Clinical Research Clinical Trials Complex Data Disease Dose Evaluable Disease External Beam Radiation Therapy Failure Foundations Funding Future Goals Hepatotoxicity Image Joint repair Joints Lesion Liver Liver parenchyma Malignant Neoplasms Maps Mathematics Methods Microspheres Modality Modeling Motivation Noise PET/CT scan Patient-Focused Outcomes Patients Performance Phase Phase I Clinical Trials Photons Physics Pilot Projects Positron-Emission Tomography Primary carcinoma of the liver cells Process Public Health Radiation Radiation Dose Unit Radiation Tolerance Radiation therapy Radioembolization Radionuclide therapy Reporting Safety Scanning Testing Toxic effect Training absorption clinical practice clinically relevant convolutional neural network deep learning denoising dosimetry image reconstruction imaging Segmentation improved innovation internal radiation learning strategy multimodal data multimodality next generation novel novel strategies personalized cancer therapy phase II trial prospective radiation delivery reconstruction response single photon emission computed tomography standard of care tool trial design tumor

项目摘要

Abstract Selective internal radiation therapy (SIRT) with preferential delivery of 90Y microspheres to target lesions has shown promising response rates with limited toxicity in the treatment of hepatocellular (HCC), the second leading cause of cancer death in the world. However, to achieve more durable responses, there is much room to improve/adapt the treatment to ensure that all lesions and lesion sub-regions receive adequate radiation delivery. While externally delivered stereotactic body radiation therapy (SBRT) is well suited for smaller solitary HCC, its application for larger or multifocal disease is challenged by the radiation tolerance of the normal liver parenchyma. A dosimetry guided combined approach that exploits complementary advantages of internal and external radiation delivery can be expected to improve treatment of HCC. To make this transition, however, prospective clinical trials establishing safety are needed. Furthermore, for routine clinic use, accurate and fast voxel-level dose estimation in internal radionuclide therapy, that lags behind external beam therapy dosimetry, is still needed. Our long-term goal is to improve the efficacy of radiation therapy with personalized dosimetry guided treatment. Our objective in this application is to demonstrate that it is possible to use 90Y imaging based absorbed dose estimates after SIRT to safely deliver external radiation to target regions (voxels) that are predicted to be underdosed and to develop deep learning based tools to make voxel-level internal dose estimation practical for routine clinic use. Specifically, in Aim 1, we will perform a Phase 1 clinical trial in HCC patients where we will take the novel approach of using the 90Y PET/CT derived absorbed dose map after SIRT to deliver SBRT to tumor regions predicted to be underdosed based on previously established dose-response models. The primary objective of the trial is to obtain estimates of safety of combined SIRT+SBRT for future Phase II trial design. In parallel, in Aim 2, building on promising initial results we will develop novel deep learning based tools for 90Y PET/CT and SPECT/CT reconstruction, joint reconstruction-segmentation and scatter estimation under the low count-rate setting, typical for 90Y. These methods have a physics/mathematics foundation, where convolutional neural networks (CNNs) are included within the iterative reconstruction process, instead of post-reconstruction denoising. In Aim 3, we will develop a CNN for fast voxel-level dosimetry and combine with the CNNs of Aim 2 to develop an innovative end-to-end framework with unified dosimetry-task based training. At the end of this study, we will be ready to use the new deep learning tools in a Phase II trial to demonstrate enhanced efficacy with SIRT+SBRT compared with SIRT alone and advance towards our long- term goal. This will accelerate adoption of these next-generation tools in clinical practice and will have a significant positive impact because treatment based on patient specific dosimetry will substantially improve efficacy, compared with current standard practice in SIRT. Although we focus on 90Y SIRT, our tools will be applicable in radionuclide therapy in general, a rapidly advancing treatment option.

抽象的选择性内部放射疗法（SIRT）优先递送90y微球向目标病变显示出有希望的反应率，在肝细胞（HCC）治疗中毒性有限，第二领先世界上癌症死亡的原因。但是，为了获得更耐用的响应，还有很大的空间改进/适应治疗，以确保所有病变和病变子区域都能获得足够的辐射输送。虽然外部交付的立体定向身体放射疗法（SBRT）非常适合较小的孤立性HCC，但它正常肝脏的辐射耐受性挑战了较大或多灶性疾病实质。剂量指导的联合方法，利用了内部和内部和可以预期外部辐射递送可以改善HCC的处理。但是，要使这个过渡需要确定安全性的前瞻性临床试验。此外，为常规诊所使用，准确而快速内部放射性核素治疗中的体素水平剂量估计，落后于外束治疗剂量测定法，仍然需要。我们的长期目标是通过个性化剂量法提高放射治疗的功效指导治疗。我们在此应用程序中的目标是证明可以使用基于90年成像的 SIRT后吸收的剂量估计值安全地向目标区域（体素）安全地传递外部辐射预计被剂量不足，并开发基于深度学习的工具来制作体素级的内部剂量常规诊所使用的估计实用。具体而言，在AIM 1中，我们将在HCC中进行1期临床试验患者将采用新颖的方法，即在SIRT后使用90Y PET/CT衍生的吸收剂量图将SBRT运送到预测基于先前确定的剂量反应被低估的肿瘤区域型号。该试验的主要目的是获得对未来联合SIRT+SBRT的安全性估算值第二阶段试验设计。同时，在AIM 2中，以有希望的初始结果为基础，我们将发展新颖的深度学习基于90Y PET/CT和SPECT/CT重建，联合重建分割和分散的工具低计数设置下的估计，通常为90年。这些方法具有物理/数学基础，卷积神经网络（CNN）包括在迭代重建过程中而不是后施加后的变态。在AIM 3中，我们将开发用于快速体素级剂量测定法的CNN和与AIM 2的CNN相结合，以统一的剂量任务开发创新的端到端框架基于培训。在这项研究结束时，我们将准备在第二阶段试验中使用新的深度学习工具与单独使用SIRT相比，SIRT+SBRT的功效增强任期目标。这将加速临床实践中这些下一代工具的采用，并将有一个重大积极影响，因为基于患者特定剂量法的治疗将大大改善与当前标准实践相比，功效。尽管我们专注于90年的sirt，但我们的工具将是通常适用于放射性核素治疗，这是一种快速前进的治疗选择。