# Quantitative imaging phenotypic classifier for distinguishing radiation effects from tumor recurrence in Glioblastoma .

> **NIH NIH R01** · CASE WESTERN RESERVE UNIVERSITY · 2022 · $72,192

## Abstract

ABSTRACT: Over 14,000 Glioblastoma (GBM) patients annually in the US undergo a combination of cranial
surgery, chemotherapy, and radiation as standard treatment for their aggressive cancer. Unfortunately, ~40% of
these patients will be identified with a suspicious lesion on a post-chemo-radiation follow up MRI scan (T1w, T2w,
FLAIR). A significant challenge in the management of GBM tumors is the differentiation of these lesions as tumor
recurrence or benign treatment-related radiation effects (TRRE). These conditions mimic each other, clinically
and radiographically. Unfortunately, in the absence of reliable diagnostic tools, patients with TRRE will undergo
an unnecessary and avoidable invasive stereotactic brain biopsy (St-Bx) for confirmation of disease absence.
However, even the invasive St-Bx has an accuracy of 85-90% due to sampling errors associated with obtaining
a biopsy tissue which may not be representative of the underlying disease pathology. Consequently, building
non-invasive decision support tools which yield a diagnostic accuracy that is non-inferior to St-Bx, represents an
attractive solution for obviating unnecessary intra-cranial St-Bx in patients with benign radiation effects.
Our group has developed a new Image-based Recurrence Risk Classifier (IRRisC) using routine MRI scans,
that has demonstrated an accuracy of 85% in distinguishing tumor recurrence from TRRE, on n=58 studies. Our
initial set of IRRisC features comprise disorder in gradient orientations on Gadolinium (Gd)-T1w MRI which have
been shown to be significantly higher in tumor recurrence compared to TRRE. Interestingly, we have recently
also demonstrated that construction of separate classifiers for males and females yielded significantly improved
prognosis of GBM survival compared to an ‘all-comers’ model. In this R01 project, we seek to further improve
and validate the accuracy of IRRisC by expanding our initial feature set (using Gd-T1w MRI) to include (1)
additional features from anatomical (T2w, FLAIR) and functional MR sequences (perfusion), (2) a new class of
biophysical deformation attributes from “normal” brain parenchyma, and (3) construction of sex-specific models
to exploit sexual-dimorphism in GBM, for distinguishing tumor recurrence from TRRE. Overcoming limitations of
previous work pertaining to small samples and lack of histopathological validation, our work will utilize the largest
multi-institutional histopathologically confirmed cohort till date of n=470 studies of TRRE and tumor
recurrence, to harmonize and validate IRRisC. Further we will establish the biological underpinning of our IRRisC
features by evaluating their association with histopathological hallmarks of TRRE and tumor recurrence. Finally,
IRRisC will be validated as decision support in a machine-reader study at 3 clinical sites. Criteria for success
for IRRisC is that it will (a) be non-inferior to the accuracy of St-Bx (~85-90%), and (b) identify no more than 50%
of patients with T...

## Key facts

- **NIH application ID:** 10375650
- **Project number:** 1R01CA264017-01A1
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Manmeet Ahluwalia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $72,192
- **Award type:** 1
- **Project period:** 2022-06-30 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10375650

## Citation

> US National Institutes of Health, RePORTER application 10375650, Quantitative imaging phenotypic classifier for distinguishing radiation effects from tumor recurrence in Glioblastoma . (1R01CA264017-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10375650. Licensed CC0.

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