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Personalized dosimetry for liver cancer radioembolization using fluid dynamics simulation

使用流体动力学模拟进行肝癌放射栓塞的个性化剂量测定

基本信息

批准号：
9899967
负责人：
Emilie Roncali
金额：
$ 16.71万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9899967
关键词：
3-Dimensional 90Y Address Affect Albumins Algorithms Anatomy Angiography Biomedical Engineering Blood Vessels Blood flow Cancer Etiology Cancer Patient Caring Catheters Cause of Death Cessation of life Classification Clinical Collaborations Convolution Kernel Custom Data Data Set Development Disseminated Malignant Neoplasm Distal Doppler Ultrasound Dose Family suidae Future Goals Hepatic Hepatic artery Human Image Incidence Individual Injections Lesion Liquid substance Liver Liver neoplasms Malignant neoplasm of liver Measures Methods Microspheres Modeling Monte Carlo Method Organ Organism Outcome Patient Care Patient imaging Patient-Focused Outcomes Patients Physicians Physics Precision therapeutics Primary Neoplasm Radiation therapy Radioactive Radioembolization Radioisotopes Radiology Specialty Radionuclide therapy Recurrence Reporting Resolution Risk Statistical Data Interpretation Structure Techniques Three-Dimensional Image Time Toxic effect Tracer Translations Treatment Efficacy Trees Unresectable Validation Variant Work base cancer type clinical implementation computerized tools convolutional neural network deep learning design dosage dosimetry hemodynamics improved innovation internal radiation morphometry multidisciplinary neglect novel novel strategies patient population personalized medicine prevent research clinical testing simulation standard of care tool treatment planning tumor

项目摘要

Project Summary/ Abstract Liver cancer is one of the leading causes of cancer deaths with rising incidence in the U.S and worldwide. Yttrium-90 microspheres radioembolization, or Selective Internal Radiation Therapy (SIRT), is a treatment in which a catheter inserted in the patient's hepatic artery delivers radioactive 90Y microspheres to the liver. It is increasingly utilized to treat patients with unresectable liver tumors in second or third line, but some of its potential to improve overall survival is still untapped. The major obstacle in making SIRT more efficient is the treatment planning. It consists in selecting the 90Y activity to inject based on the estimated dose to the tumor and organs-at-risk. The problem is that the dose calculation is highly unreliable and does not include important parameters, such as well-known non-uniformities or the injection point. As a result, physicians often choose very conservative dosage to limit toxicity at the expense of the tumor(s) dose, which drastically reduces SIRT efficacy. The objective of this project is to develop accurate patient-specific dosimetry for SIRT planning. We propose a novel method combining computational fluid dynamics (CFD) to simulate the 90Y microsphere 3D distribution and 90Y physics modeling to predict the absorbed dose. The central and novel approach is to carry out the CFD simulations for each patient's hepatic arterial tree to achieve high accuracy and precision, because anatomical features determining the microsphere distribution present wide variations across the patient population and prohibit the use of generic models. This novel CFD-based dosimetry will be the first comprehensive tool to integrate (1) the hepatic arterial tree extracted from the patient's standard-of-care angiogram, (2) CFD simulation in this hepatic arterial tree to predict and optimize the microsphere distribution, (3) calculation of the absorbed dose with 90Y physics modeling. Our long-term goal is developing a tool that can be integrated in clinical workflow to optimize the quantity and injection point of 90Y microspheres during SIRT planning. To this end, we will pursue two specific aims. (1) We will develop the CFD model and dose calculation using a pig model for validation; (2) we will develop a deep learning approach to simultaneously segment the hepatic artery from the standard-of-care patient angiograms and conduct a morphometric study of the obtained hepatic arterial trees to identify the principal parameters affecting the model. If successful, this project will generate a reliable, patient-specific dosimetry for SIRT providing a comprehensive calculation of the absorbed dose in individual lesions as well as in the healthy liver. This will enable high precision treatment planning to better treat the tumors with a “dose-painting” approach and ultimately improve long-term patient outcome.

项目总结/摘要肝癌是癌症死亡的主要原因之一，在美国和世界范围内发病率不断上升。钇-90微球放射性栓塞，或选择性内部放射治疗（SIRT），是一种治疗，插入患者肝动脉的导管将放射性90 Y微球输送到肝脏。它越来越多地用于治疗二线或三线不可切除的肝肿瘤患者，但其一些提高总生存率的潜力仍未得到开发。提高SIRT效率的主要障碍是治疗计划它包括基于对肿瘤的估计剂量选择要注射的90 Y活性和危险器官问题是剂量计算非常不可靠，重要的参数，如众所周知的不均匀性或注入点。因此，医生通常选择非常保守的剂量来限制毒性，而牺牲肿瘤剂量，降低SIRT的疗效。本项目的目标是为SIRT计划制定准确的患者特异性剂量测定。我们提出结合计算流体力学（CFD）对90 Y微球进行三维模拟的新方法分布和90 Y物理模型来预测吸收剂量。核心和新颖的方法是对每个患者的肝动脉树进行CFD模拟，以实现高准确度和精确度，因为决定微球分布的解剖学特征在整个组织中呈现出很大的变化，患者群体，并禁止使用通用模型。这种新的基于CFD的剂量测定将是第一个综合工具，用于整合（1）从患者标准护理中提取的肝动脉树血管造影，（2）该肝动脉树中的CFD模拟，以预测和优化微球分布， (3)用90 Y物理模型计算吸收剂量。我们的长期目标是开发一种工具，可以整合到临床工作流程中，以优化90 Y微球的数量和注射点， SIRT计划。为此，我们将追求两个具体目标。(1)我们将开发CFD模型和剂量使用猪模型进行验证的计算;（2）我们将开发一种深度学习方法，从标准护理患者血管造影照片中分割肝动脉并进行形态测量研究以识别影响模型的主要参数。如果成功，该项目将为SIRT提供可靠的患者特异性剂量测定，综合计算单个病变以及健康肝脏中的吸收剂量。这将实现高精度的治疗计划，以更好地治疗肿瘤的“剂量绘画”的方法，最终改善患者的长期预后。