# Quantitative Oncologic PET-MR

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $457,390

## Abstract

PROJECT SUMMARY
The overall goal of this competing renewal application remains focused on developing and leveraging
quantitative molecular imaging biomarkers that take advantage of a synergy between PET and MR imaging to
provide significantly improved guidance for cancer radiation therapy (RT) in sarcomas. Traditionally, definition
of the radiotherapy target volume utilizes CT and (sometimes) MR images to delineate visible gross tumor
volume (GTV) which is expanded by certain margins to account for microscopic disease according to clinical
guidelines, forming the clinical target volume (CTV). The standard T1/T2 weighted MR images are unable to
distinguish tumor infiltration from inflammation and therefore inclusion of edema into the CTV is left to the
physician's judgment.
For patients unable to have resection, definitive RT results in local control of only 40 to 75% with the failures
occurring primarily within the gross tumor. Standard anatomic imaging does not allow identification of areas at
risk of local failure. We hypothesize that multiparametric PET/MR imaging will help to identify potential areas of
resistant cell populations within the tumor in order to prescribe the higher dose to these areas to increase
treatment effectiveness.

## Key facts

- **NIH application ID:** 9873924
- **Project number:** 5R01CA165221-07
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Georges El Fakhri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $457,390
- **Award type:** 5
- **Project period:** 2012-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9873924

## Citation

> US National Institutes of Health, RePORTER application 9873924, Quantitative Oncologic PET-MR (5R01CA165221-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9873924. Licensed CC0.

---

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