# Quantitative MRI to automatically delineate intraprostatic tumor for radiation therapy

> **NIH NIH R01** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2024 · $704,563

## Abstract

PROJECT SUMMARY
Prostate cancer (PCa) is the most common cancer among men. Radiation therapy is an integral part of the
standard of care for the treatment of PCa. Local recurrences of PCa after radiation therapy usually originate
from the primary tumor site. A dose escalation to the clinically significant tumor (csT) foci in intermediate- and
high-risk patients is reported to result in significantly improved biochemical disease-free survival (bDFS)
without increasing radiation toxicity. Contouring of the intraprostatic tumor is currently based on
multiparametric (mp-) magnetic resonance imaging (MRI) and the interpretation of the MRI results are based
on Prostate Imaging Reporting & Data System (PI-RADS). However, relatively large variability in performance
of mp-MRI, including that of PI-RADS, continue to pose barriers to identify clinically significant prostate tumor
as treatment target and improve patient outcome. In this project, we propose to address the issues in both the
MRI technique and scoring system aspects and develop a tool that can automatically delineate intraprostatic
csT and be easily transferred to clinical use. To achieve this goal, in Aim 1, we will build a quantitative MRI
(QMRI) toolset including magnetic resonance fingerprinting (MRF), arterial spin labeling (ASL), and diffusion-
weighted MRI (DW-MRI). This toolset provides 3-dimensional high-resolution quantitative maps directly
associated with tumor physiology and pathology features. In Aim 2, based on the QMRI output, we will develop
an automated PCa tumor probability model and segmentation method that can automatically differentiate areas
with clinically significant prostate cancer from other prostatic tissues. Aim 3 will be a validation study. We will
validate the performance of our newly developed method against biopsy results and compare it with the
performance of physician's contour of csT foci based on mp-MRI and PI-RADS as the current standard of care
(SOC). We expect that the results of this project will provide a completely new set of QMRI tools and tumor
segmentation method that improves current SOC mp-MRI and PI-RADS. This will allow us to accurately
identify areas with clinically significant PCa in the intact prostate for boost irradiation, and achieve maximal
local control in prostate cancer radiation therapy without increasing radiation toxicity.

## Key facts

- **NIH application ID:** 10982242
- **Project number:** 1R01CA284172-01A1
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** Jia Guo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $704,563
- **Award type:** 1
- **Project period:** 2024-07-01 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10982242

## Citation

> US National Institutes of Health, RePORTER application 10982242, Quantitative MRI to automatically delineate intraprostatic tumor for radiation therapy (1R01CA284172-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10982242. Licensed CC0.

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