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.

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