Optimizing Efficiency and Quality of Brachytherapy for Cervical Cancer using Machine Learning Based Automation

使用基于机器学习的自动化优化宫颈癌近距离放射治疗的效率和质量

基本信息

批准号：
10645003
负责人：
Sandra Michelle Meyers
金额：
$ 24.78万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-14 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10645003
关键词：
3-Dimensional Affect Algorithms Anatomy Automation Blinded Brachytherapy Cervical Cessation of life Clinical Clinical Treatment Clinical Trials Clinical Trials Design Complex Consumption Country Data Decision Making Dose Ethics Evaluation Foundations Future Goals Grant Human Image Inferior Institution Knowledge Label Link Machine Learning Malignant Neoplasms Malignant neoplasm of cervix uteri Malignant neoplasm of prostate Manuals Measures Medical Mentors Mentorship Methods Modeling Modernization Organ Output Patient imaging Patient-Focused Outcomes Patients Physicians Positioning Attribute Process Public Health Radiation Radiation Dose Unit Radiation therapy Radioactive Radiotherapy Research Research Research Personnel Resources Sedation procedure Source Standardization Statistical Data Interpretation Statistical Models System Techniques Technology Testing Time Toxic effect Training Training Programs Validation Variant Woman Work automated treatment planning cancer therapy cancer type career development clinical efficacy clinical implementation combat computer program computerized tools convolutional neural network deep learning efficacy evaluation experience image processing implementation barriers implementation science improved innovation knowledge base machine learning model malignant breast neoplasm optimal treatments predictive modeling pressure prospective skills standard of care statistics technology validation three-dimensional modeling tool treatment planning tumor

项目摘要

Current treatment planning for brachytherapy of cervical cancer is performed with manual techniques that are both time-consuming and subjective. Manual treatment planning takes 95 minutes on average and occurs while patients are sedated, and the quality of the treatments is highly dependent on the expertise of the physician. Unfortunately, the resource intensiveness and need for specialized expertise are barriers to implementation of brachytherapy, and as a result many centers are not offering this essential treatment for cervical cancer. Alarmingly, this rapid decline in brachytherapy utilization has been linked to 12% reductions in patient survival. To overcome the barriers to delivering highly effective brachytherapy, there is a critical need for tools that improve the efficiency and reduce the complexity of treatment planning for each patient. My long- term goal is to become an independent investigator focused on automating brachytherapy cancer treatment with machine learning, producing button-click solutions that will significantly upgrade the quality of brachytherapy and combat declining utilization. I have significant experience in modeling, image processing and computer programming and I want to build on this skillset with a training program that will prepare me for independence. I have assembled an exceptional mentorship team, which includes expertise in machine learning, clinical trials, implementation science and statistics. We formed a training plan to gain expertise in (1) deep learning, (2) advanced statistical analysis, (3) design of clinical trials and implementation of technology and (4) research career development. The research goal of this proposal is to develop a tool for fully automated cervical brachytherapy treatment planning, which uses machine learning models to make predictions for new patients. The central hypothesis is that automated planning using machine learning will generate non-inferior or even superior plans in significantly reduced treatment planning time. This hypothesis will be tested with the following specific aims: (1) Develop machine learning models, which use labelled patient images to predict radiation dose; (2) Develop and evaluate efficacy of a pipeline for automated brachytherapy planning; and (3) Prospectively measure the efficiency and clinical impact of automated brachytherapy planning. For Aim 1, convolutional neural networks will be developed to predict 3D radiation dose from imaging. Aim 2 will convert predicted doses into deliverable treatment plans using gradient-descent optimization to determine optimal treatment parameters. Aim 3 will provide an end-to-end validation of the automated planning by testing it in real-time clinical workflow. This work is innovative because it presents the first clinical validation of an automated treatment planning system for brachytherapy of cervical cancer. The proposed research is significant because it will revolutionize the current brachytherapy paradigm by applying machine learning to automate and standardize time-consuming, manual processes. This work is a key step towards my future R01 submission on multi-institutional implementation of automated cervical brachytherapy.

目前宫颈癌近距离放射治疗的治疗计划是通过手动技术进行的，这些技术既耗时又主观。手动治疗计划平均需要 95 分钟才能完成当患者服用镇静剂时，治疗的质量在很大程度上取决于医生的专业知识医生。不幸的是，资源密集度和对专业知识的需求是实现这一目标的障碍。实施近距离放射治疗，因此许多中心不提供这种基本治疗宫颈癌。令人担忧的是，近距离放射治疗利用率的迅速下降与 12% 的减少有关患者生存。为了克服提供高效近距离放射治疗的障碍，迫切需要寻找能够提高每位患者治疗计划的效率并降低其复杂性的工具。我的长- 短期目标是成为一名专注于自动化近距离放射治疗癌症治疗的独立研究者通过机器学习，生成按钮式解决方案，将显着提高质量近距离放射治疗和战斗利用率下降。我在建模、图像处理方面拥有丰富的经验和计算机编程，我想通过一个培训计划来巩固这一技能，为我做好准备独立。我组建了一支出色的指导团队，其中包括机器方面的专业知识学习、临床试验、实施科学和统计。我们制定了一项培训计划，以获得 (1) 方面的专业知识深度学习，（2）高级统计分析，（3）临床试验设计和技术实施 (4) 研究职业发展。本提案的研究目标是开发一种工具来充分自动化颈椎近距离放射治疗治疗计划，使用机器学习模型来制定对新患者的预测。中心假设是使用机器学习的自动规划将在显着减少治疗计划时间的情况下生成不劣质甚至更优的计划。这个假设将针对以下具体目标进行测试：（1）开发机器学习模型，该模型使用标记的患者预测辐射剂量的图像； (2) 开发和评估自动近距离放射治疗管道的功效规划; (3) 前瞻性衡量自动近距离放射治疗的效率和临床影响规划。对于目标 1，将开发卷积神经网络来预测 3D 辐射剂量成像。目标 2 将使用梯度下降将预测剂量转化为可实施的治疗计划优化以确定最佳治疗参数。目标 3 将提供端到端验证通过在实时临床工作流程中进行测试来实现自动化规划。这项工作具有创新性，因为它呈现了宫颈癌近距离放射治疗自动化治疗计划系统的首次临床验证。这拟议的研究意义重大，因为它将通过应用彻底改变当前的近距离放射治疗范式机器学习可实现耗时的手动流程的自动化和标准化。这项工作是关键一步面向我未来提交的关于多机构实施自动颈椎近距离放射治疗的 R01。