# Detection of prostate Cancer Specific Signals with Hybrid Multi-Dimensional MRI

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2022 · $523,484

## Abstract

ABSTRACT
There is a critical need for new alternatives for screening and diagnosis of prostate cancer (PCa). Current
methods for detecting and diagnosing prostate cancer (PCa), including serum PSA level, DRE (digital rectal
exam), and TRUS-guided (transrectal ultrasound) random prostate biopsy are seriously flawed since they are
unreliable and lead to procedures that often do not help and frequently harm patients, at high financial costs.
MRI has potential to improve detection and management of PCa, due to its excellent soft tissue contrast and
functional information. Nevertheless there is, as of yet, no MRI method that is adequate for routine screening or
for guiding biopsies. To be clinically useful –MRI must identify clinically significant cancers (Gleason 7 or
higher) and distinguish them from normal prostate, benign changes, and Gleason 6 ‘cancers’.
In this resubmission, we propose to extend our previous work on hybrid multi-dimensional MRI (HM-MRI),
based on the combination of T2-weighted and diffusion-weighted imaging. This approach is very different from
conventional MRI measurements of T2 and ‘apparent diffusion coefficient’ (ADC). Conventional methods treat
T2 and ADC as independent parameters. In contrast, HM-MRI measures the change in T2 as a function of ‘b’
value, and the change in ADC as a function of ‘TE’. HM-MRI exploits the interdependence of T2 and ADC and
distinct MR properties of prostate tissue components to increase diagnostic accuracy of PCa diagnosis.
We will analyze HM-MRI data to extract volume fractions of the luminal, epithelial, and stromal compartments,
and the ADC and T2 of each compartment in each image voxel. Volume fractions of these tissue
compartments, when measured using quantitative histology, are known to provide high diagnostic accuracy.
This proposal is significantly revised to respond to the previous review. We will test the hypotheses that:
1. HM-MRI data can identify clinically significant PCa, by non-invasively measuring epithelial, stromal, and
luminal volume fractions, to provide information similar to quantitative histology.
2. In addition, HM-MRI provides the T2 and ADC of each compartment, and the volume and spatial distribution
of these compartments. This information may increase diagnostic accuracy, and cannot be easily obtained from
histology.
As a result, HM-MRI combined with compartmental analysis can be used clinically to provide high diagnostic
accuracy, and non-invasive assessment of PCa aggressiveness.

## Key facts

- **NIH application ID:** 10365985
- **Project number:** 5R01CA228036-04
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Gregory S. Karczmar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $523,484
- **Award type:** 5
- **Project period:** 2019-04-03 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10365985

## Citation

> US National Institutes of Health, RePORTER application 10365985, Detection of prostate Cancer Specific Signals with Hybrid Multi-Dimensional MRI (5R01CA228036-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10365985. Licensed CC0.

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