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: 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
• 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

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用手动技术进行的， 既费时又主观。手动治疗计划平均需要95分钟， 当病人被镇静时，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，资源密集和对专门知识的需要是实现这一目标的障碍。 近距离放射治疗的实施，因此，许多中心不提供这种基本的治疗， 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12%的死亡率下降有关。 患者生存率。为了克服提供高效近距离放射治疗的障碍， 为每位患者提高治疗计划的效率并降低其复杂性。我的长- 长期目标是成为一名独立的研究人员，专注于自动化近距离放射治疗癌症治疗 通过机器学习，生产按钮点击解决方案，这将大大提升 近距离放射治疗和对抗使用率下降。我在建模，图像处理， 和计算机编程，我想建立在这个技能与培训计划，将我准备 独立我组建了一个出色的导师团队，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，以获得以下方面的专业知识： 深度学习，（2）高级统计分析，（3）临床试验设计和技术实施 （4）科研生涯发展。本提案的研究目标是开发一种工具， 自动宫颈近距离放射治疗计划，使用机器学习模型， 对新患者的预测核心假设是，使用机器学习的自动规划将 在显著减少的治疗计划时间内生成非劣或甚至上级计划。这一假设 将测试以下具体目标：（1）开发机器学习模型，使用标记的患者 图像预测辐射剂量;（2）开发和评估自动近距离放射治疗管道的有效性 计划;和（3）Proximity测量自动近距离放射治疗的效率和临床影响 规划对于Aim 1，将开发卷积神经网络来预测3D辐射剂量， 显像目标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 优化以确定最佳治疗参数。目标3将提供对 通过在实时临床工作流程中进行测试来实现自动规划。这项工作是创新的，因为它提出了 宫颈癌近距离放射治疗自动治疗计划系统的首次临床验证。的 拟议的研究是重要的，因为它将彻底改变目前的近距离放射治疗模式， 通过机器学习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 关于多机构实施自动宫颈近距离放射治疗的未来R 01提交资料。