Early-Stage Clinical Trial of AI-Driven CBCT-Guided Adaptive Radiotherapy for Lung Cancer

AI驱动的CBCT引导的肺癌适应性放疗的早期临床试验

• 批准号: 10575081
• 负责人: Aparna Kesarwala
• 金额: $ 19.56万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10575081
• 关键词: Air; Anatomy; Bronchial Tree; Cancer Patient; Chest; Clinic; Clinical; Data; Databases; Dimensions; Dose; Dose Limiting; Early treatment; Equipment; Generations; Goals; Heart; Image; Immobilization; Learning; Linear Accelerator Radiotherapy Systems; Link; Lung; Malignant neoplasm of lung; Methodology; Methods; Modernization; Modification; Motion; Network-based; Non-Small-Cell Lung Carcinoma; Organ; Outcome; Patient Care; Patients; Pattern; Phase I Clinical Trials; Physicians; Positioning Attribute; Primary Neoplasm; Process; Radiation; Radiation Oncology; Radiation therapy; Relative Risks; Research; Research Personnel; Resources; Risk; Role; Scanning; Secondary to; Supervision; System; Testing; Time; Tissues; Toxic effect; Uncertainty; Validation; Vertebral column; X-Ray Computed Tomography; artificial intelligence method; automated segmentation; clinical risk; cone-beam computed tomography; convolutional neural network; cost; deep learning; early phase clinical trial; effective therapy; feasibility trial; imaging Segmentation; improved; interest; learning strategy; loss of function; novel; participant enrollment; primary endpoint; prospective; quantitative imaging; secondary outcome; simulation; standard of care; success; treatment duration; treatment planning; tumor

PROJECT SUMMARY Stereotactic body radiation therapy (SBRT) is a highly effective treatment for early-stage non-small cell lung cancer, but its accuracy can be compromised by multiple factors. There is an interval between simulation and the first day of treatment, the size and position of targets and organs at risk can shift over a course of treatment, and the thorax is in constant multidimensional motion. Adaptive radiation can improve the accuracy of SBRT, but implementing it within the workflow of a busy radiation oncology clinic currently requires re- simulation and re-planning, costing valuable departmental time and resources. Cone beam computed tomography (CBCT) scans are obtained daily prior to the delivery of each fraction, but their utility for adaptive radiation therapy has been limited by their image quality. Processing time also remains a significant barrier for real-time deep learning-based methodologies. The objective of our proposed research is therefore to develop, validate, and test in an early clinical trial the feasibility of using our two-part cone-beam computed tomography- based deep learning method for dose verification based on rapid and accurate generation of high quality synthetic CTs and multi-organ segmentation. In this project, we will pursue two Specific Aims: 1) to develop and refine CBCT-based synthetic CTs for CBCT quality improvement, and 2) to evaluate the clinical feasibility of our synthetic CT-based dose verification. The early clinical trial will prospectively enroll patients with early- stage non-small cell lung cancer receiving definitive SBRT. Validation of the feasibility of this method is a necessary intermediate step towards our longer-term goal of the implementation of real-time lung cancer adaptive radiation, which will allow for increased accuracy of higher dose to target volumes and lower doses to organs at risk, thereby improving local control and decreasing radiation-related risks and toxicities for patients with non-small cell lung cancer.

项目概要 立体定向放射治疗（SBRT）是治疗早期非小细胞肺的高效方法 癌症，但其准确性可能会受到多种因素的影响。仿真与仿真之间有一个间隔 在治疗的第一天，有风险的目标和器官的大小和位置可能会在一个疗程中发生变化 治疗时，胸部处于持续的多维运动中。自适应辐射可以提高精度 SBRT，但在繁忙的放射肿瘤诊所的工作流程中实施它目前需要重新 模拟和重新规划，花费了宝贵的部门时间和资源。锥束计算 每天在交付每个部分之前进行断层扫描 (CBCT) 扫描，但它们对于适应性的实用性 放射治疗因其图像质量而受到限制。处理时间仍然是一个重大障碍 基于实时深度学习的方法。因此，我们提出的研究的目标是开发， 在早期临床试验中验证和测试使用我们的两部分锥形束计算机断层扫描的可行性- 基于深度学习方法的剂量验证基于快速准确生成高质量 合成 CT 和多器官分割。在这个项目中，我们将追求两个具体目标：1）开发 并完善基于 CBCT 的合成 CT 以提高 CBCT 质量，2) 评估临床可行性 我们基于合成 CT 的剂量验证。早期临床试验将前瞻性地招募早期患有以下疾病的患者： 接受确定性 SBRT 的非小细胞肺癌分期。验证该方法的可行性 实现实时肺癌的长期目标的必要中间步骤 自适应辐射，这将提高对目标体积的较高剂量和对目标体积的较低剂量的准确性 处于危险中的器官，从而改善局部控制并降低患者与辐射相关的风险和毒性 患有非小细胞肺癌。