# A tissue viability imaging biomarker for use in non-invasive breast cancer therapy

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $555,975

## Abstract

Summary/Abstract
While improved early detection methods and treatments have reduced breast cancer mortality, a sizable
portion of patients remains overdiagnosed and overtreated, warranting the development of more conservative
breast cancer treatments. Magnetic resonance guided focused ultrasound (MRgFUS) is one of the most
attractive, emerging procedures for breast cancer as it can safely and efficaciously treat localized breast
tumors non-invasively. Currently, clinical MRgFUS ablation treatments are assessed with MRI metrics that
primarily quantify thermal and vascular effects. While there is evident MR sensitivity for tissue changes induced
by MRgFUS thermal ablation, no single metric or combination of metrics have demonstrated adequate
accuracy in predicting tissue viability during or immediately post-MRgFUS ablation treatment. In addition, the
use of gadolinium contrast agent-based assessment techniques precludes further ablation treatment if positive
tumor margins are suspected. This work proposes to address this critical unmet need through developing a
deep neural network non-contrast imaging biomarker that would provide an immediate and accurate
assessment of tissue viability and could be applied repeatedly for an iterative assessment of tissue viability
during the MRgFUS ablation procedure, assuring complete non-invasive tumor treatment. This objective will be
accomplished with three specific aims.
Aim 1: Develop and validate a 3D multiparametric MRI protocol for efficient acquisition of qualitative and
quantitative MR images in the breast MRgFUS therapeutic environment.
Aim 2: Develop, train and validate a deep neural network biomarker for predicting tissue viability in a tumor
model during MRgFUS ablation treatments.
Aim 3: Integrate the tissue viability biomarker in an existing breast MRgFUS ablation clinical workflow and
demonstrate complete treatment volume ablation using the non-contrast, deep neural network biomarker as
the treatment assessment metric.
We have developed an innovative, volumetric histopathology diffeomorphic registration procedure that allows
the voxel-wise comparison of in vivo MR images to histopathological data, providing the gold-standard labeled
data set needed to develop this imaging biomarker. Training and validation of the imaging biomarker will be
performed in preclinical models designed to allow immediate generalizability and translation to ongoing clinical
trials. This imaging biomarker will provide accurate assessment of tissue viability during MRgFUS ablation
treatments, revolutionizing minimally invasive breast cancer treatments and directly addressing the critical
issue of overtreatment.

## Key facts

- **NIH application ID:** 10774280
- **Project number:** 5R01CA259686-03
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** SARANG JOSHI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $555,975
- **Award type:** 5
- **Project period:** 2022-02-10 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10774280

## Citation

> US National Institutes of Health, RePORTER application 10774280, A tissue viability imaging biomarker for use in non-invasive breast cancer therapy (5R01CA259686-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10774280. Licensed CC0.

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