# Imaging-based tumor forecasting to predict brain tumor progression and response to therapy

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2024 · $1

## Abstract

The vision for this program is to develop tumor forecasting methods to predict and optimize the response of
glioblastoma multiforme to standard-of-care therapies—and do so on a tumor-specific basis. A fundamental
challenge in the care of patients with brain tumors is the limitation of standard radiographic methods to
accurately evaluate, let alone predict, patient response. We propose to address this shortcoming by developing
predictive, biologically-based mathematical models that incorporate the hallmark characteristics of brain tumor
growth (e.g., tumor induced angiogenesis, hypoxia, necrosis, proliferation, invasion, and resistance to therapy)
that can be initialized using advanced, subject-specific imaging data. This project will address two critical gaps
in the care of patients battling brain cancer. First, our imaging-based, mathematical framework accounts for
subject-specific characteristics and treatment regimens on model predictions. Second, in most studies, the
ground truth used for validation of the predictive model is whether the model can predict future regional
contrast enhancement, despite the well-known limitations of this qualitative MRI feature. Thus, while prior
human studies have demonstrated the potential of predictive modeling, its translation into a realistic radiologic
tool is fundamentally hindered by lack of systematic, pre-clinical validation where critical tumor characteristics
(e.g., tumor heterogeneity and whole brain tumor cell distribution) can be precisely known and rigorously
controlled. To overcome these limitations, we aim to: 1) establish the accuracy of tumor-specific modeling to
predict spatiotemporal progression and 2) establish the accuracy of tumor-specific modeling to predict
therapeutic response. Experimentally, we will construct a family of mathematical models that employ
quantitative MRI data to capture the fundamental biological features of glioblastoma. These data are
longitudinally acquired in patient derived xenografts that are treatment naïve or undergoing radiotherapy and/or
chemotherapy. The model family is then calibrated with these data and a novel model selection strategy is
employed to choose the most parsimonious model for predicting the spatio-temporal evolution of each tumor
which is then compared to MRI data collected at future time points. Model predictions of tumor progression
will be validated via registration to 3D fluorescent images of cleared ex vivo tissue, a technique that enables
visualization of whole brain tumor burden. We will provide the clinical and scientific community with a
validated mathematical description of glioma progression that can reliably predict progression and therapy
response across a range of relevant glioma signaling pathways and can be readily applied to the clinical setting.

## Key facts

- **NIH application ID:** 10895438
- **Project number:** 5R01CA260003-03
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Christopher Chad Quarles
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2022-09-19 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10895438

## Citation

> US National Institutes of Health, RePORTER application 10895438, Imaging-based tumor forecasting to predict brain tumor progression and response to therapy (5R01CA260003-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10895438. Licensed CC0.

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