# Green Diversity Supplement: Predicting 5-ALA Fluorescence Status in High Grade Gliomas Based on MRI Features

> **NIH NIH U54** · MAYO CLINIC ARIZONA · 2024 · $44,937

## Abstract

Abstract
High-grade gliomas (HGGs), such as glioblastoma multiforme (GBM), are notably aggressive,
heterogeneous, and infiltrating brain tumors, presenting significant challenges in surgical
resection and leading to poor survival rates. Despite advancements in surgical techniques,
radiation, and chemotherapy, the invasive nature and high recurrence rate of HGGs limit
treatment effectiveness. Fluorescence-guided surgery with 5-aminolevulinic acid (5-ALA) offers
high specificity and sensitivity in tumor margin delineation but is hindered by limitations such as
false negatives due to photobleaching, obstruction by other tissues, low tumor cell density, and
non-efficient dosage timing. These challenges can leave active tumor regions after surgery that
could lead to recurrence and complicate subsequent treatments. Additionally, T1-weighted
imaging with gadolinium-based contrast (T1Gd) often underestimates the tumor burden,
particularly in non-enhancing regions, leading to residual disease. Addressing these challenges,
our study aims to identify MRI features correlating with 5-ALA positive and negative areas in
HGGs to develop a radiomics model predicting 5-ALA fluorescence on preoperative MRI scans.
Our central hypothesis is that a radiomics model can predict 5-ALA fluorescence from MRI
features in glioblastoma patients, and when considering sex differences, further refine its
accuracy. With the proposed model, we intend to improve preoperative planning and surgical
outcomes by accurately identifying tumor margins. Furthermore, we will evaluate the model's
prognostic utility by linking 5-ALA fluorescence predictions to the extent of tumor resection and
survival rates. Given the emerging evidence of HGGs as a sexually dimorphic disease, our
study will also explore sex differences in model development, anticipating significant impacts on
predictive accuracy and survival outcomes. The project aims to provide surgeons with objective
evidence to assess tumor burden, plan surgeries more effectively, and improve survival
outcomes across glioma patient groups. The project proposed here will be an extension of
existing work by the Mathematical Neuro-Oncology (MNO) Lab (Parent Project PI: Dr. Kristin
Swanson), utilizing ongoing research in image-localized biopsies, MRI-based invasion mapping,
and image-based model development.

## Key facts

- **NIH application ID:** 11064719
- **Project number:** 3U54CA274504-01A1S1
- **Recipient organization:** MAYO CLINIC ARIZONA
- **Principal Investigator:** Peter Canoll
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $44,937
- **Award type:** 3
- **Project period:** 2023-09-18 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11064719

## Citation

> US National Institutes of Health, RePORTER application 11064719, Green Diversity Supplement: Predicting 5-ALA Fluorescence Status in High Grade Gliomas Based on MRI Features (3U54CA274504-01A1S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11064719. Licensed CC0.

---

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