# MOSAIC: Imaging Human Tissue State Dynamics In Vivo

> **NIH NIH U54** · MAYO CLINIC ARIZONA · 2023 · $342,900

## Abstract

SUMMARY: PROJECT 2: IMAGING THE DYNAMIC TISSUE STATE IN PATIENTS IN VIVO
With a dismal median survival of 16 months, glioblastoma (GBM) is the most common malignant primary brain
tumor within adult patients. Response to the standard-of-care (SOC) is widely variable across patients.
Identifying optimal targeted treatments traditionally relies on tissue sampling to identify patient-relevant targets.
Yet, tissue sampling has many severe limitations and costs (time, money, and facility access), and ultimately
provides only limited scope both spatially and temporally thus always leaving behind residual tumor cells that
have not been sampled. Multi-parametric magnetic resonance imaging (MRI) measures an array of
complementary physiologic biomarkers that correspond with diverse tumor phenotypes (e.g., proliferation,
inflammation, angiogenesis), and it serves as the clinical mainstay for monitoring therapeutic response and
disease progression. As tumor cell signaling may be mediated through interactions (i.e.,“cross-talk”) with
surrounding non-tumoral cells in the regional microenvironment, there is a critical need to define the degree to
which this cross-talk influences local tissue state, phenotypic expression, and disease
progression. Understanding these associations should help refine the clinical interpretations of imaging
phenotypes to improve guidelines for non-invasive diagnosis and disease monitoring. There is an urgent need
for image-based radiomics tools that can 1) predict which patients will respond to a given treatment and 2) can
observe/track that response over time.
Overall Hypothesis: Tissue states, represented as combinations of cellular constituents and phenotypes, can be
resolved on clinical imaging to a level sufficient to identify transitions in these states with and without treatments
in individual patients in vivo.
Our two aims in this project investigate this hypothesis in two separate settings, Aim 1) Standard of Care, Aim
2) Immunotherapy. In these aims, we will characterize the landscape of phenotypic states, build image-based
models to predict tissue state from images, investigate how predicted tumor states correspond with outcomes,
quantify dynamics of states from pre- to post-therapy, and finally build mechanistic models to understand the
critical driving differences in the flow of cells in local phenotype state space leading to the overall tumor state.

## Key facts

- **NIH application ID:** 10729423
- **Project number:** 1U54CA274504-01A1
- **Recipient organization:** MAYO CLINIC ARIZONA
- **Principal Investigator:** Kristin R Swanson
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $342,900
- **Award type:** 1
- **Project period:** 2023-09-18 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10729423

## Citation

> US National Institutes of Health, RePORTER application 10729423, MOSAIC: Imaging Human Tissue State Dynamics In Vivo (1U54CA274504-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10729423. Licensed CC0.

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