# Radiomic spatial maps for identifying viable tumor extent on multi-parametric MRI for Glioblastoma

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $639,642

## Abstract

ABSTRACT: Glioblastoma (GBM) has a complex infiltrating tumor microenvironment which extends well beyond
the visible enhancing tumor margins and plays a substantial role in GBM recurrence and poor outcomes.
Unfortunately, in the absence of a precise spatial map of tumor extent, it is often difficult to differentiate infiltrating
tumor from vasogenic edema on clinical MRI during radiation/surgical planning. The untreated infiltrating tumor
ultimately contributes to over 90% of GBM recurrences. An equally pressing challenge is the difficulty in
distinguishing recurrent tumor from treatment-effects following chemoradiation. Due to the histologically diverse
landscape of post-treated lesions, treatment-effects often co-exist with tumor recurrence, and mimic appearance
on imaging. In the absence of reliable tools, 15-20% of patients with GBM recurrence are incorrectly diagnosed
due to sampling error associated with intracranial biopsy. Thus, developing a non-invasive spatial map of
GBM tumor extent that can reliably identify infiltrating/recurrent tumor from confounding pathologies (treatment-
effects/edema), will have significant implications in radiation/surgical-planning and post-treatment management.
Recently, we developed a Radiomic-Image (Rad-I) map of tumor extent that uses computational features
corresponding to the micro-architectural image measurements of disorder in the local intensity gradients (i.e.,
gradient entropy). The initial version of the Rad-I map has been evaluated to distinguish recurrent tumors versus
treatment-effects on post-treatment Gd-T1w MRI with an 85% accuracy on n=75 studies, and to distinguish
infiltrating tumor versus vasogenic edema on pre-treatment MRI scans with a 94% accuracy on n=42 studies.
In this R01 project, we propose to improve on our initial version of Rad-I map by incorporating (1) additional
anatomical (T2w, FLAIR) and functional MR sequences (perfusion) and (2) a novel “lesion complexity” feature,
which captures organizational changes in the tissue composition via graph-theoretic approaches on MRI scans.
Overcoming limitations pertaining to small cohorts and lack of spatially mapped ex-vivo histology for validation,
Rad-I maps will be extensively validated on (1) a large multi-institutional MRI cohort with co-localized
histopathology and (2) the PRESERVE clinical trial designed to capture GBM heterogeneity via multiple co-
localized tissue samples/lesion. These cohorts will also allow for establishing associations of our new radiomic
features with underlying histological/molecular tumor characteristics- a prerequisite for clinical adoption. Lastly,
Rad-I maps will be evaluated within a tumor board survey to address the clinically challenging problem of
distinguishing recurrent tumors versus treatment effects. Criteria for success for Rad-I maps are that they are
at least non-inferior to the accuracy of stereotactic biopsies (85-90%) in identifying tumor niches corresponding
to (a) viable/infiltrating t...

## Key facts

- **NIH application ID:** 10812012
- **Project number:** 1R01CA277728-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Manmeet Ahluwalia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $639,642
- **Award type:** 1
- **Project period:** 2024-06-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10812012

## Citation

> US National Institutes of Health, RePORTER application 10812012, Radiomic spatial maps for identifying viable tumor extent on multi-parametric MRI for Glioblastoma (1R01CA277728-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10812012. Licensed CC0.

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