# Brain Cancer Radio-Pathomics for Predicting Heterogeneous Cytology

> **NIH NIH R01** · MEDICAL COLLEGE OF WISCONSIN · 2021 · $352,275

## Abstract

Abstract
High-grade brain cancer (glioblastoma) is a devastating disease that very few patients survive long-term. The
average life expectancy is 15 months, and throughout therapy patients undergo serial MR imaging for
monitoring tumor response. It is not well understood how heterogeneity at the cellular and molecular levels
affects the macroscopic imaging characteristics of these tumors.
 The long-term goal of this project is to provide imaging tools and biomarker integration strategies for
individualizing glioblastoma treatment. The overall objective is to combine radiographic imaging with
histopathological samples (i.e., radio-pathomics) to create and validate predictive tools for accurately defining
tumor margins and spatial molecular profiles. Our central hypothesis is that microscopic glioblastoma
cytological features and spatially dependent molecular profiles are reliably detectable and quantifiable with
macroscopic MR imaging. Two specific aims will objectively test this hypothesis by first determining which
microscopic tissue features contribute to distinct measurements with MR imaging, and second, determining
the performance of machine learning algorithms for predictively mapping these heterogeneous histological
features.
!

## Key facts

- **NIH application ID:** 10173711
- **Project number:** 5R01CA218144-05
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** Peter S LaViolette
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $352,275
- **Award type:** 5
- **Project period:** 2017-06-09 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173711

## Citation

> US National Institutes of Health, RePORTER application 10173711, Brain Cancer Radio-Pathomics for Predicting Heterogeneous Cytology (5R01CA218144-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10173711. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*