# Quantitative (Perfusion and Diffusion) MRI Biomarkers to Measure Glioma Response

> **NIH NIH U01** · MEDICAL COLLEGE OF WISCONSIN · 2020 · $559,217

## Abstract

Abstract
 The continuing goal of our research program is to optimize and disseminate effective imaging-based
strategies to personalize brain tumor treatment. Current Response Assessment in NeuroOncology (RANO)
criteria, which incorporate anatomic imaging only, are insufficient for distinguishing tumor from treatment effect
(TE). Without definitive confirmation of tumor progression, no treatment changes are recommended for several
months after standard therapies. Thus, patients are precluded from switching to potentially more effective
therapies—a limitation that could be overcome with more reliable imaging techniques.
 To this end, during the previous funding cycle, we demonstrated the feasibility of several quantitative imaging
(QI) tools to reliably distinguish tumor from treatment effect and predict treatment response. These QI tools
include a machine-learning approach to calibrate T1w images enabling the creation of quantitative delta T1
(qDT1) maps. The qDT1 enable the detection of true contrast enhancing lesion volume (CELV). The qDT1
together with our proven dynamic susceptibility contrast (DSC) MRI methods, for determination of rCBV (relative
cerebral blood volume), are used to generate a new biomarker, fractional tumor burden (FTB), to delineate the
extent of tumor within CELV on a voxel-wise basis. These perfusion-based QI tools in combination with our
diffusion MRI technology, which includes functional diffusion maps (FDMs) and more recently RSI (restriction
spectrum imaging), provide a comprehensive assessment of brain tumor and its distinction from treatment effect.
 Now, in order to translate this technology for use in clinical trials and daily practice, some final updates and
clinical validation studies are needed as proposed here. First, to ease adoption and testing in the clinical setting
improvements are proposed for the individual QI technologies along with the development of a streamlined
workflow (Aim 1). To improve the widespread adoption of DSC-MRI and FTB biomarker, studies will be
performed to confirm that a single-dose DSC-MRI method can replace the standard double-dose method without
affecting the accuracy of rCBV or the creation of FTB maps (Aim 1.1). Also, registration and segmentation
algorithms will be updated to include deformable registration and recent advances in deep learning for
longitudinal reporting of CELV, non-enhancing lesion volumes (NELV) and each of the QI metrics (Aim 1.2).
Finally, a streamlined workflow that incorporates these improvements will be created (Aim 1.3). The Aim 2 studies
will test the QI tools and workflow using clinical trial data (Aim 2.1-2.2) and daily clinical practice (Aim 2.3-2.4).
 Clinical validation of this new QI-RANO workflow, with evidence showing improved prediction in comparison
to current measures, has the potential to cause a paradigm shift in how brain tumor burden is assessed.

## Key facts

- **NIH application ID:** 10006506
- **Project number:** 5U01CA176110-07
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** KATHLEEN Marie SCHMAINDA
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $559,217
- **Award type:** 5
- **Project period:** 2014-02-28 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10006506

## Citation

> US National Institutes of Health, RePORTER application 10006506, Quantitative (Perfusion and Diffusion) MRI Biomarkers to Measure Glioma Response (5U01CA176110-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10006506. Licensed CC0.

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