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: 10764140
• 负责人: Wensha Yang
• 金额: $ 23.14万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10764140
• 关键词: Advanced; prediction; GBM; recurrence; TIME

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.

抽象的： 多形胶质母细胞瘤（GBM）是成年人中最常见的原发性脑恶性肿瘤。 GBM患者 对包括手术切除，放疗（RT），化学疗法和肿瘤治疗领域在内的疗法的反应 （TTF）不令人满意，导致高复发率，这被认为是致命的。打捞RT通常习惯 延迟复发性GBM肿瘤的生长和延长患者生存。但是，由于 GBM细胞添加了大型各向同性治疗缘（〜2cm），以覆盖超出的微观疾病 射线照相确认的磁共振图像（MRI）。因为重叠很大 在循环和初级计划目标量（PTV）之间，额外打捞的生长延迟 正常器官耐受性允许的辐射剂量适中。更重要，更有效的剂量是有毒的 面向有风险的器官（OARS），包括脑干，CHIASM，视神经和涉及的脑实质等 安全地升级剂量，必须大大减少复发量。与 放射学确认的肿瘤具有添加的非特异性边缘，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生态位（SCN）在 GBM细胞的迁移显示了voxel the的可行性GBM复发的可行性2-3个月前2-3个月 在射线照相上显而易见。 GBM复发（时间）算法的预测为 通过训练纵向多参数随访的机器学习分类器MR开发 图像，量化大脑中复发与SCN之间的潜在连接。鉴于有希望的 结果，有必要进一步改善算法以更准确的预测并确定其对 介入临床试验之前的放疗治疗计划。提出以下目标 实现目标。目标1：开发一个由干细胞生态裂位置弱监督的神经网络以执行 体素级复发预测。 AIM 2A：前瞻性患者图像数据获取，预处理和 Voxel复发预测模型验证。 AIM 2B：证明可以显着剂量升级 实现了早期的复发。该项目的成功将进一步阐明SCN参与GBM， 提供一种预测复发的方法，并帮助提高打捞的靶向准确性和功效 放疗。最后一点将为实践的前瞻性介入试验铺平道路 - 更改GBM管理。