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.

概括 磁共振成像引导适应性放射治疗 (MRgART) 可以更安全地治疗其他疾病 难以治疗的腹部软组织癌，例如发生在接近的无法手术的胰腺癌 高度活动且对放射敏感的胃肠（GI）器官。 MRgART 可以每天重新规划以进行补偿 通过改善肿瘤和附近器官的可视化来发现器官形状变化。然而，附近 腹部器官在治疗期间和治疗期间会发生很大的移动，并且至关重要的是，准确的跟踪 目前尚无法了解这些组织中累积的剂量分布。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。我们假设准确估计周围移动健康器官的剂量， 在所有分数上累积，将使整个范围的治疗不那么保守且更有效 的疾病。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 局部肿瘤进展和发病率，特别是邻近胃肠道器官的肿瘤，是 开发可靠准确的变形图像配准（DIR）方法来估计空间剂量 在整个治疗过程中，从之前的分数中积累到移动胃肠道器官。这个提议 通过开发、严格验证和系统地测量目标增益来满足关键需求 利用一组虚拟数字双胞胎，通过创新的深度学习 DIR 剂量累积进行覆盖。在 目标 1，我们将开发针对特定患者的虚拟数字双胞胎队列，对 21 种不同的时间变化进行建模 逼真的胃肠道运动，包括呼吸和消化运动。双胞胎将结合分析建模 与广泛使用的 XCAT 数字模型。在目标 2 中，虚拟数字孪生将用于优化和 严格验证我们针对胃肠道器官的创新渐进式配准分割深度学习网络。 这种方法的关键技术新颖之处在于它能够执行时空变化的正则化 模拟大变形，这对于大多数 DIR 方法来说是不可能的。在目标 3 中，使用 AI 的潜在临床收益 与高剂量区域保守限制的临床标准相比，DIR 剂量累积将 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，经过验证可用于真实的生理胃肠道运动，将适用 除了胰腺肿瘤之外，还将适用于其他胃肠道软组织癌症。最终，良好的可用性 经过验证的剂量累积技术可以使临床医生定量确定累积的剂量 辐射剂量分布到腔内胃肠道器官并适当考虑溢出辐射，从而导致 更个性化、更安全、可能更有效的放射治疗。

项目成果

Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT.

危险胃肠器官的逐步细化深度关节配准分割 (ProRSeg)：在 MRI 和锥束 CT 中的应用。

• 发表时间: 2023-08
• 影响因子: 3.8
• 作者: Jiang, Jue;Hong, Jun;Tringale, Kathryn;Reyngold, Marsha;Crane, Christopher;Tyagi, Neelam;Veeraraghavan, Harini
• 通讯作者: Veeraraghavan, Harini