# Imaging signatures of genetic mutations in glioblastoma using machine learning

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $670,422

## Abstract

Glioblastoma (GB) is the most common and aggressive malignant adult brain tumor, with grim
prognosis and heterogeneous molecular and imaging profiles. Although the currently applicable
treatment options (i.e., surgery, radiotherapy, chemotherapy) have expanded during the last 20
years, there is no substantial improvement in the OS rates. The major obstacle in treating GBM
patients is the heterogeneity of their molecular landscape. Determination of molecular targets
requires ex vivo postoperative tissue analyses, which are limited in assessing the tumor's spatial
heterogeneity (sampling error due to single sample histopathological and molecular analysis) and
temporal heterogeneity (not possible to continuously assess the molecular transformation of the
tumor during treatment). Herein we propose to develop quantitative imaging phenomic (QIP)
markers of a range of mutations of interest in GB. We will build on prior work on EGFR, IDH1
mutations and MGMT methylation QIP signatures, and develop an extensive panel of imaging
signatures of 10 gene mutations, as well as MGMT promoter methylation, using machine learning
methods applied to relatively routine clinical mpMRI (standard plus diffusion tensor and perfusion
protocols). Availability of such biomarkers can contribute to non-invasive i) patient stratification
into appropriate treatments, ii) measurement of individual molecular characteristics. In particular,
we propose to carry out the following specific aims:
Specific Aim 1 (SA1): To develop the enabling methodologies for constructing Quantitative
Imaging Phenomic signatures of GB mutations
Specific Aim 2 (SA2): Establish QIP signatures of 10 mutations of interest in gliomas, plus
MGMT promoter status, using next generation sequencing (NGS). We will use 709 datasets.
Specific Aim 3 (SA3): Characterize the molecular heterogeneity of GB using QIP signatures,
leveraging the NGS samples of SA1, as well as a new sample that we will genotype, adding to a
total of 600 tumor samples obtained from 4 different locations per patient from 150 patients. The
first 150 tissue samples are already analyzed as part of ongoing work.
Specific Aim 4 (SA4): Integrate our methods into the Cancer Imaging Phenomics Toolkit
(CaPTk), in order to allow easy access to them by users

## Key facts

- **NIH application ID:** 9893291
- **Project number:** 2R01NS042645-16A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Christos Davatzikos
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $670,422
- **Award type:** 2
- **Project period:** 2002-06-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9893291

## Citation

> US National Institutes of Health, RePORTER application 9893291, Imaging signatures of genetic mutations in glioblastoma using machine learning (2R01NS042645-16A1). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/9893291. Licensed CC0.

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