Artificial Intelligence powered virtual digital twins to construct and validate AI automated tools for safer MR-guided adaptive RT of abdominal cancers

人工智能支持虚拟数字双胞胎来构建和验证人工智能自动化工具，以实现更安全的 MR 引导的腹部癌症自适应放疗

• 批准号: 10736347
• 负责人: Neelam Tyagi
• 金额: $ 37.48万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10736347
• 关键词: 4D MRI; Abdomen; Accounting; Address; Artificial Intelligence; Cancer Etiology; Cessation of life; Cine Magnetic Resonance Imaging; Clinical; Communities; Compensation; Complex; Data; Data Set; Development; Disease; Dose; Ensure; Failure; Gastrointestinal tract structure; Geometry; Image; Imaging technology; Institution; Local Therapy; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of abdomen; Malignant neoplasm of pancreas; Measures; Medical; Methods; Modeling; Morbidity - disease rate; Motion; Multi-Institutional Clinical Trial; Organ; Palliative Care; Patients; Periodicity; Peristalsis; Phase; Physiological; Qualifying; Radiation; Radiation Dose Unit; Radiation Tolerance; Radiation Toxicity; Radiation therapy; Research; Research Infrastructure; Resected; Risk Reduction; Safety; Shapes; Stomach; Survival Rate; Techniques; Testing; Time; Tissues; Toxic effect; Training; Translational Research; Tumor Volume; Twin Multiple Birth; Unresectable; Validation; Variant; Visualization; advanced pancreatic cancer; cohort; comorbidity; deep learning; design; digital; digital twin; effective therapy; gastrointestinal; image guided; image registration; improved; innovation; learning network; novel; pancreatic cancer patients; pancreatic neoplasm; reconstruction; respiratory; soft tissue; spatiotemporal; tool; tumor; tumor progression; usability; virtual

SUMMARY Magnetic resonance imaging-guided adaptive radiotherapy (MRgART) allows for safer treatment of otherwise difficult-to-treat soft-tissue cancers in the abdomen, such as inoperable pancreatic cancers that occur close to highly mobile and radiosensitive gastrointestinal (GI) organs. MRgART enables daily replanning to compensate for organ shape variations through improved visualization of the tumor and nearby organs. However, nearby abdominal organs move considerably between and during treatment fractions and, crucially, accurate tracking of the dose distribution accumulated in those tissues is currently unavailable. Consequently, tumor prescription coverage is still often constrained to sub-optimal levels by design to conservatively reduce the risk of radiation toxicity to GI organs. We hypothesize that accurate estimates of doses to the surrounding mobile healthy organs, accumulated over all fractions, would enable a less conservative and more effective treatment of the full extent of the disease. Hence, the key clinical need we will address, to ensure improved local control and to reduce rates of local tumor progression and morbidity, particularly in the tumors adjacent to luminal GI organs, is the development of reliably accurate deformable image registration (DIR) methods to estimate the spatial dose accumulated to the mobile GI luminal organs throughout treatment from previous fractions. This proposal addresses the key need by developing, rigorously validating, and systematically measuring the gain in target coverage with an innovative deep learning DIR dose accumulation utilizing a cohort of virtual digital twins. In Aim 1, We will develop patient-specific virtual digital twin cohorts modeling 21 different temporally varying realistic GI motions encompassing respiratory and digestive motion. The twins will combine analytical modeling with the widely used XCAT digital phantoms. In Aim 2, the virtual digital twins will be used to optimize and rigorously validate our innovative progressive registration-segmentation deep learning network for GI organs. The key technical novelty of this approach is its ability to perform spatio-temporally varying regularization to model large deformations, not possible with most DIR methods. In Aim 3, the potential clinical gain of using AI- DIR dose accumulation compared with the clinical standard with conservative limits to the high dose region will be systematically simulated with a variety of GI tract motion using the VDT datasets. Potential impact: The developed and validated AI-DIR techniques, validated for realistic physiologic GI motions, will be applicable beyond pancreatic tumors and will apply to other GI soft-tissue cancers. Ultimately, the availability of well- validated dose accumulation techniques could enable clinicians to quantitatively determine the accumulated radiation dose distribution to luminal GI organs and appropriately account for the spillover radiation, thus leading to more personalized, safer, and possibly more effective radiation treatments.

摘要 磁共振成像引导的适应性放射治疗(MRgART)允许更安全的治疗 难以治疗的腹部软组织癌，如发生在 高度可移动和对辐射敏感的胃肠道(GI)器官。MRgART支持每日重新计划以补偿 通过改进对肿瘤和附近器官的可视化来实现器官形状的变化。然而，在附近 腹部器官在治疗部分之间和期间有相当大的移动，而且至关重要的是，准确的跟踪 目前还无法获得这些组织中累积的剂量分布的数据。因此，肿瘤处方 为了保守地降低辐射风险，覆盖范围仍然经常被限制在次优水平。 对胃肠道器官的毒性。我们假设，对周围可移动健康器官的准确剂量估计， 累积在所有部分上，将能够实现不那么保守和更有效的全面治疗 这种疾病的危害。因此，我们将解决的关键临床需求是，确保改善局部控制并降低发病率 局部肿瘤的进展和发病率，特别是在与胃肠道器官相邻的肿瘤中， 可靠精确的可变形图像配准(DIR)空间剂量估计方法的发展 在治疗过程中从先前的部分累积到可移动的胃肠道器官。这项建议 通过开发、严格验证和系统测量目标收益来满足关键需求 覆盖范围与创新的深度学习DIR剂量积累利用虚拟数字双胞胎队列。在……里面 目标1，我们将开发针对患者的虚拟数字双胞胎队列，模拟21个不同的时间变化 真实的胃肠道运动，包括呼吸和消化运动。这对双胞胎将结合分析建模 与被广泛使用的xCAT数字模体。在AIM 2中，虚拟数字双胞胎将被用来优化和 严格验证我们创新的GI器官渐进式注册-分割深度学习网络。 这种方法的关键技术新颖性是它能够执行时空变化的正则化 建模大变形，这在大多数DIR方法中是不可能的。在目标3中，使用人工智能的潜在临床收益- 与临床标准相比，DIR剂量累积将保守地限制在高剂量区 使用VDT数据集系统地模拟各种胃肠道运动。潜在影响： 开发和验证的AI-DIR技术，经验证可用于真实的生理性GI运动 不仅适用于胰腺肿瘤，还将适用于其他胃肠道软组织癌。最终，油井的可用性- 经过验证的剂量累积技术可以使临床医生定量地确定累积的 辐射剂量分布到管腔胃肠道器官，并适当地解释溢出辐射，从而导致 到更个性化、更安全、可能更有效的放射治疗。