# Using Connectomics and Machine Learning to Predict Survival in Diffuse Glioma

> **NIH NIH R03** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $93,081

## Abstract

ABSTRACT
Diffuse gliomas are the most common malignant primary brain tumors. Clinical outcomes, including overall
survival, vary significantly across individual patients and are not adequately explained by known prognostic
factors such as age, histologic and molecular pathology, and imparted treatments. A deeper understanding of
prognostic influences in diffuse glioma can facilitate therapeutic decision making and patient counselling and
lend further insight into the biologic underpinnings of the disease. Magnetic resonance imaging (MRI) scans of
the brain are part of standard pre-operative evaluation in these patients. As brain structure and function are
modulated by biologic and environment factors, MRI-derived metrics often provide sensitive prognostic
biomarkers. We have preliminarily shown that connectomics, a method of measuring brain connectivity from
MRI, can accurately predict survival in patients with diffuse glioma. We will retrospectively obtain at least
1,150 datasets and attempt to validate our preliminary models (Aim 1). We will also examine connectome
phenotypes associated with tumor genotypes to help refine and improve our models (Aim 2). Accurate pre-
operative prediction of outcome may ultimately allow for better tailored interventions for the individual patient
and assist clinicians in optimizing both tumor control and neurologic function in treatment decision making.

## Key facts

- **NIH application ID:** 10289350
- **Project number:** 1R03CA241862-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** SHELLI R KESLER
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $93,081
- **Award type:** 1
- **Project period:** 2021-09-21 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10289350

## Citation

> US National Institutes of Health, RePORTER application 10289350, Using Connectomics and Machine Learning to Predict Survival in Diffuse Glioma (1R03CA241862-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10289350. Licensed CC0.

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