# Advanced prediction of GBM recurrence (TIME) for personalized radiotherapy

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $179,860

## Abstract

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.

## Key facts

- **NIH application ID:** 10679035
- **Project number:** 5R21CA267139-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Wensha Yang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $179,860
- **Award type:** 5
- **Project period:** 2022-08-08 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10679035

## Citation

> US National Institutes of Health, RePORTER application 10679035, Advanced prediction of GBM recurrence (TIME) for personalized radiotherapy (5R21CA267139-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10679035. Licensed CC0.

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