Advanced prediction of GBM recurrence (TIME) for personalized radiotherapy

GBM 复发 (TIME) 的高级预测以进行个性化放疗

• 批准号: 10512641
• 负责人: Wensha Yang
• 金额: --
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-08 至 2022-10-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10512641
• 关键词: Adult; Algorithms; Anatomy; Attention; Benefits and Risks; Brain; Brain Stem; Cancer Patient; Cells; Clinical; Clinical Trials; Collaborations; Coupling; Data; Diagnosis; Diffuse; Disease; Dose; Early Intervention; Enrollment; Excision; Future; Glioblastoma; Goals; Growth; Intervention; Intervention Trial; Label; Location; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Neoplasms; Manuals; Microscopic; Modeling; Nature; Nervous System Physiology; Operative Surgical Procedures; Optic Nerve; Organ; Patient imaging; Patient-Focused Outcomes; Patients; Performance; Primary Brain Neoplasms; Primary Neoplasm; Radiation Dose Unit; Radiation therapy; Radiology Specialty; Recurrence; Recurrent tumor; Research; Research Personnel; Risk; Role; Supervision; Testing; Time; Training; Validation; aggressive therapy; base; brain parenchyma; cell motility; chemotherapy; data acquisition; deep learning model; design; experience; follow-up; high risk; improved; machine learning classifier; neural network; novel; patient response; predictive modeling; prospective; radiological imaging; stem cell niche; stem cells; success; treatment planning; treatment response; trial comparing; tumor; tumor growth

Abstract: Glioblastoma multiforme (GBM) is the most common primary brain malignancy in adults. GBM patients' response to therapies including surgical resection, radiotherapy (RT), chemotherapy, and tumor treating fields (TTF) is unsatisfactory, leading to a high recurrence rate, which is considered fatal. Salvage RT is often used to delay recurrent GBM tumor growth and prolong patient survival. However, due to the diffusive nature of the GBM cells, a large isotropic treatment margin (~2cm) is added to cover microscopic disease beyond the radiographically confirmed tumor on magnetic resonance image (MRI). Because of the considerable overlap between the recurrent and primary planning target volumes (PTV), growth delay from the additional salvage radiation dose allowed by the normal organ tolerance is modest. A more significant, more effective dose is toxic to organs at risk (OARs), including the brain stem, chiasm, optic nerves, and involved brain parenchyma, etc. To safely escalate the dose, the recurrent treatment volume must be significantly reduced. Compared with the radiologically confirmed tumor with added non-specific margin, the volume of the subclinical recurrence at an earlier time point is markedly smaller. Our preliminary research based on the role of stem cell niches (SCN's) in GBM cell migration shows the feasibility of voxel-wise prediction of GBM recurrences 2-3 months before they become radiographically apparent. The prediction of the GBM recurrence (TIME) algorithm was developed through training a machine learning classifier on longitudinal multi-parametric follow-up MR images, quantifying the potential connection between the recurrence and SCN's in the brain. Given the promising results, it is necessary to further improve the algorithm for more accurate prediction and establish its impact on radiotherapy treatment planning before an interventional clinical trial. The following aims are proposed to achieve the goal. Aim 1: Develop a neural network weakly supervised by stem cell niche locations to perform voxel-level recurrence prediction. Aim 2a: Prospective patient image data acquisition, pre-processing, and voxel-wise recurrence prediction model validation. Aim 2b: Demonstrate that significant dose escalation can be achieved for early predicted recurrence. The project's success will further elucidate SCN's involvement in GBM, provide a way to early predict recurrence, and help improve the targeting accuracy and efficacy of salvage radiotherapy. The last point will pave a path towards a prospective interventional trial that can be practice- changing for GBM management.

摘要： 多形性胶质母细胞瘤（GBM）是成人最常见的原发性脑恶性肿瘤。GBM患者 对手术切除、放疗（RT）、化疗和肿瘤治疗领域等治疗的反应 (TTF)不令人满意，导致高复发率，这被认为是致命的。抢救RT通常用于 延迟复发性GBM肿瘤生长并延长患者生存期。然而，由于其扩散性质， GBM细胞，添加大的各向同性处理边缘（~2cm），以覆盖GBM细胞以外的显微镜下疾病。 在磁共振成像（MRI）上放射学证实的肿瘤。由于大量的重叠 经常性和主要规划目标量（PTV）之间，额外救助的增长延迟 正常器官耐受所允许的辐射剂量是适度的。更显著更有效的剂量是有毒的 危及器官（OAR），包括脑干、视交叉、视神经和受累脑实质等。 为了安全地递增剂量，必须显著减少复发性治疗量。较 放射学证实的肿瘤，增加了非特异性边缘，亚临床复发的体积， 较早的时间点明显较小。我们的初步研究基于干细胞龛（SCN）在 GBM细胞迁移显示了在GBM复发前2 - 3个月按体素预测GBM复发的可行性， 在放射学上变得明显。GBM复发（TIME）算法的预测是 通过在纵向多参数随访MR上训练机器学习分类器开发 图像，量化复发和大脑中SCN之间的潜在联系。考虑到 结果，有必要进一步改进算法，以便更准确地预测并建立其对 介入性临床试验前的放射治疗计划。提出了以下目标， 达到目的。目标1：开发一个由干细胞小生境位置弱监督的神经网络， 体素级递归预测。目标2a：前瞻性患者图像数据采集、预处理和 逐体素递归预测模型验证。目的2b：证明可以显著增加剂量 实现早期预测复发。该项目的成功将进一步阐明SCN在GBM中的参与， 提供一种早期预测复发的方法，并有助于提高靶向准确性和挽救疗效 放疗最后一点将为可以实践的前瞻性干预性试验铺平道路- GBM管理的变化。