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; 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.

概括 磁共振成像引导的自适应放射疗法（MRGART）允许更安全的治疗 腹部难以治疗的软组织癌，例如未经手术的胰腺癌 高度移动和放射敏感的胃肠道（GI）器官。 MRGART使每天的重新登录可以补偿 通过改善肿瘤和附近器官可视化的器官形状变化。但是，附近 腹部器官在治疗分数之间和期间大大移动，并且至关重要的是准确的跟踪 目前不可用这些组织中积累的剂量分布。因此，肿瘤处方 通过设计，覆盖范围通常仍被限制为次优水平，以保守降低辐射的风险 对GI器官的毒性。我们假设对周围移动健康器官剂量的准确估计， 在所有分数上积累，将使整个范围都能保持不太保守，更有效的待遇 疾病。因此，我们将解决的关键临床需求，以确保改善本地控制并降低费率 局部肿瘤的进展和发病率，特别是在腔内胃肠道附近的肿瘤中，是 开发可靠准确的可变形图像注册（DIR）方法来估计空间剂量 从以前的分数中整个处理中积累到移动的胃肠腔器官。这个建议 通过开发，严格验证并系统地衡量目标的增益来满足关键需求 利用一系列虚拟数字双胞胎的群体，具有创新的深度学习DIR剂量积累的覆盖范围。在 AIM 1，我们将开发特定于患者的虚拟数字双胞胎队列，建模21个不同的时间变化 现实的胃肠道动作包括呼吸和消化运动。双胞胎将结合分析建模 使用广泛使用的XCAT数字幻像。在AIM 2中，虚拟数字双胞胎将用于优化和 严格验证我们针对GI器官的创新渐进式注册细分深度学习网络。 这种方法的关键技术新颖性是其执行时空变化的正则化的能力 模型大变形，大多数DIR方法不可能。在AIM 3中，使用AI的潜在临床增益 与具有保守限制到高剂量区域的临床标准相比，DIR剂量的积累将 使用VDT数据集使用各种GI道运动系统地模拟。潜在影响： 为现实的生理GI运动验证的开发和验证的AI-DIR技术将适用 除了胰腺肿瘤之外，还将适用于其他GI软组织癌。最终，可用性 经过验证的剂量积累技术可以使临床医生能够定量确定累积 辐射剂量分布到腔gi器官，并适当说明溢出辐射，因此导致 更具个性化，更安全，可能更有效的辐射治疗。