# MR Fingerprinting and Computerized Decision Support for Prostate Cancer

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $581,727

## Abstract

PROJECT SUMMARY
The prostate remains the only organ in which blind untargeted biopsies are conducted without a pre-identified
suspected focus of neoplasm. Men with palpable abnormalities or elevated prostate specific antigen must
endure trans-rectal biopsy with related costs, discomfort, stress, and complications, because it is not
impossible to objectively and reliably identify who does not require a biopsy. Well over 500,000 men in the US
with no evidence of prostate cancer still undergo prostate biopsies, solely on account of PSA, a grade D test
according to the USPTF. Consequently there is clearly an unmet need to develop both better imaging
techniques and image analysis algorithms that can enable improved non-invasive characterization of prostate
cancer and distinguish low grade indolent cancers from the more aggressive intermediate to high grade
variants. This would help channel and monitor appropriate patients in less aggressive treatment protocols such
as active surveillance.
Currently MRI is excellent for detecting high grade prostate cancer (PCa), but is less accurate for low and
intermediate grade disease. Definitive exclusion of disease, and thus the need for biopsy in a subset of
patients, is not possible. Also, patients who opt for active surveillance cannot be followed by imaging alone and
require repeated periodic biopsy. Magnetic resonance fingerprinting (MRF) is a framework pioneered by our
team for simultaneously quantifying multiple tissue properties with MRI, and has been used to quantify T1 and
T2 more efficiently, accurately, and precisely than previously possible. Extensive preliminary data show the
utility of this technology in combination with apparent diffusion coefficient (ADC) mapping, to separate normal
peripheral zone from potential cancer. In parallel our team has been developing and validating computerized
decision support (CDS) tools which can diagnose, grade, and characterize PCa both in the peripheral and
transitional zones on MRI. We propose to develop an MRF exam for prostate cancer that allows
simultaneous mapping of T1, T2, and ADC for efficient and quantitative separation of PCa from normal
prostate and to separate low risk and more aggressive disease. We will also develop integrated CDS
tools to identify additional image derived features (radiomics) from the MRF derived maps to
complement MRF measurements for more accurate detection and grading of PCa both in the peripheral
and transitional zones. The accuracy of this combined MRF+CDS exam will be prospectively validated in a
cohort of 250 men scanned prior to biopsy.

## Key facts

- **NIH application ID:** 10219975
- **Project number:** 5R01CA208236-05
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Vikas Gulani
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $581,727
- **Award type:** 5
- **Project period:** 2017-03-02 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10219975

## Citation

> US National Institutes of Health, RePORTER application 10219975, MR Fingerprinting and Computerized Decision Support for Prostate Cancer (5R01CA208236-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10219975. Licensed CC0.

---

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