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

• 批准号: 10351221
• 负责人: Sandra Michelle Meyers
• 金额: $ 25.04万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 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/10351221
• 关键词: 3-Dimensional; Affect; Algorithms; Anatomy; Automation; Blinded; Brachytherapy; Cervical; Clinical; Clinical Treatment; Clinical Trials; Clinical Trials Design; Complex; Consumption; Country; Data; Decision Making; Dose; Ethics; Evaluation; Foundations; Future; Goals; Grant; Human; Image; Inferior; Knowledge; Label; Lead; 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; base; cancer therapy; cancer type; career development; clinical efficacy; clinical trial 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; pressure; prospective; standard of care; statistics; technology validation; three-dimensional modeling; tool; treatment planning; tumor

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执行的，这些技术 既耗时又主观。人工治疗计划平均需要95分钟，并发生 虽然患者服用了镇静剂，但治疗的质量高度依赖于 医生。不幸的是，资源密集度和对专门知识的需求是阻碍 实施近距离放射治疗，因此许多中心不提供这种必要的治疗 宫颈癌。令人担忧的是，近距离放射治疗使用率的快速下降与 病人存活率。要克服提供高效近距离放射治疗的障碍，迫切需要 用于提高每个患者的治疗计划的效率和降低复杂性的工具。我的龙- 学期目标是成为一名专注于癌症近距离放射治疗自动化的独立调查者 通过机器学习，产生可显著提升质量的按钮点击解决方案 近距离放射治疗和对抗使用率下降。我在建模、图像处理方面有丰富的经验 和计算机编程，我想在这一技能的基础上，通过一个培训计划来为我做好准备 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、实施科学和统计学。我们制定了一个培训计划，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)研究事业发展。这项建议的研究目标是开发一种工具，以充分 自动宫颈近距离放射治疗计划，它使用机器学习模型来制作 对新病人的预测。中心假设是，使用机器学习的自动计划将 在显著减少治疗计划时间的情况下生成非劣质甚至更好的计划。这一假设 将在以下特定目标下进行测试：(1)开发机器学习模型，其中使用标签患者 用于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疗效 计划；以及(3)前瞻性地测量自动化近距离放射治疗的效率和临床影响 计划。对于目标1，将开发卷积神经网络来预测3D辐射剂量 成像。AIM 2将使用梯度下降法将预测剂量转换为可交付的治疗计划 优化以确定最佳处理参数。AIM 3将提供端到端验证 通过在实时临床工作流程中测试它来实现自动规划。这项工作具有创新性，因为它呈现了 首次临床验证用于宫颈癌近距离放射治疗的自动治疗计划系统。这个 拟议的研究具有重要意义，因为它将通过应用 机器学习可实现耗时的手动流程的自动化和标准化。这项工作是关键的一步 关于我未来关于多机构实施自动化宫颈近距离放射治疗的R01提交。