# Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery

> **NIH NIH P20** · UNIVERSITY OF OKLAHOMA · 2022 · $242,225

## Abstract

Project 1: Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery
ABSTRACT
 Surgical resection is one of the primary treatments for human gliomas, and a growing number of studies have
demonstrated the benefits of maximal safe resection for patient survival. However, the decision of surgical
resection of tumor-infiltrated brain tissue is often difficult given the risk of inducing neurological deficits. Tumors
with ill-defined boundaries that invade and/or infiltrate eloquent areas are often incompletely resected or deemed
inoperable for fear of conferring a debilitating deficit. Nonetheless, it is increasingly acknowledged that the
functional anatomy of the human neocortex is plastic. Dramatic reorganization of functional brain regions, such
as language cortices, have been seen in patients with infiltrating tumors such as gliomas, suggesting such
patients with tumors invading functional brain areas may in fact be surgical candidates. Because it has been
demonstrated that progression free survival (PFS) and overall survival (OS) of patients correlate with extent of
resection in surgery, patients may benefit from a more aggressive surgical strategy that accounts for the
information of functional recovery after surgery, i.e. neural plasticity. The focus of this research project is to
develop an intelligent and multimodal strategy for identifying plasticity based on images of brain connectivity that
relates to the neurological deficits after surgery in patients with focal brain gliomas involving motor and/or
language regions. Three imaging modalities including resting-state functional magnetic resonance imaging,
diffusion tensor imaging and navigated transcranial magnetic stimulation (nTMS) will be used and integrated to
identify new imaging markers. The project has three Specific Aims. In patients following surgery for
motor/speech area gliomas, we will identify plasticity metrics based on multimodal connectivity mapping and
determine the relationship between plasticity metrics and neurological deficits (Aim 1) and determine whether
baseline connectivity maps and extent of resection can be used to predict plasticity (Aim 2). In addition, we will
develop an intelligent, machine learning based model that predicts the probability of long-term deficits and overall
survival (Aim 3). The success of this project can demonstrate feasibility of developing a novel multimodal-based
quantitative image marker to predict clinical outcome of brain tumor surgery and acquire the solid preliminary
data to support the research project leader (RPL) to apply for a more comprehensive NIH R01 project that aims
to further optimize and validate the new multimodality imaging technology and prediction model. The long-term
outcomes of the research effort will lead to a comprehensive understanding of neural plasticity after surgery
and develop new quantitative neuroimaging clinical markers based on the machine learning models to assist
prediction of PFS or OS of pati...

## Key facts

- **NIH application ID:** 10334985
- **Project number:** 1P20GM135009-01A1
- **Recipient organization:** UNIVERSITY OF OKLAHOMA
- **Principal Investigator:** Han Yuan
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $242,225
- **Award type:** 1
- **Project period:** 2022-02-15 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10334985

## Citation

> US National Institutes of Health, RePORTER application 10334985, Neuroimaging Markers for Predicting Outcome of Brain Tumor Surgery (1P20GM135009-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10334985. Licensed CC0.

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