# Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $576,312

## Abstract

Project summary
Computational multi-scale modeling is a growing area of research that aims to link whole slide images and
radiographic iamges with multi-omics molecular profiles of the same patients. Multi-scale modeling has shown
its potential through its ability to predict clinical outcomes e.g. prognosis, and through predicting actionable
molecular properties of tumors, e.g. the activity of EGFR, a major drug target in many cancers. Current
applications are limited to study associations between imaging and molecular data, and predicting long term
outcomes. No actionable information can be gained from multi-scale biomarkers yet.
We propose to develop a multi-scale modeling framework to support treatment response, treatment monitoring
and treatment allocation for patients with brain tumors, focusing on the most aggressive subtype of glioma, IDH
wild-type high grade glioma. In Aim 1, we will develop informatics algorithms that integrate multi-scale data for
treatment response. We will use our expertise in data fusion and develop novel approaches to integrate multi-
scale data to predict first line treatment response. In Aim 2, we will develop algorithms that allow combining
multi-scale data at diagnosis with multi-modal MR imaging data during treatment follow-up. We will focus on
predicting treatment response and progression and whether we can predict these events earlier than
radiologists can. In Aim 3, we will develop algorithms that use the multi-scale data to predict drug target
activities and also suggest novel drugs for patients that become resistant to first line treatment. We will use a
mixture of publicly available glioma multi-scale data sets totaling more than 1000 patients, and also 1600
retrospective and 150 prospective brain tumor patients from Stanford Medical Center.
Combining these complementary data sources in a multi-scale framework for data fusion can have profound
contributions toward predicting treatment outcomes by uncovering unknown synergies and relationships. More
specifically, developing computational models integrating quantitative image features and molecular data to
develop multi-scale signatures, holds the potential to translate in benefit to brain tumor patients by investigating
biomarkers that accurately predict treatment response. Readily, because whole slide images and radiographic
imaging is part of the routine diagnostic work-up of cancer patients and molecular data of brain tumors is
increasingly being used in clinical workflows, therefore if reliable multi-scale signatures can be found reflecting
treatment response, translation to clinical applications is feasible, including optimizing recruitment for clinical
trials.

## Key facts

- **NIH application ID:** 10614974
- **Project number:** 5R01CA260271-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Olivier Gevaert
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $576,312
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10614974

## Citation

> US National Institutes of Health, RePORTER application 10614974, Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation (5R01CA260271-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10614974. Licensed CC0.

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