# A 3D DCE-US Interconnected Voxel Analysis Framework for Bedside Characterization of Tumor Vascular Properties in Liver Malignancies.

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $599,191

## Abstract

PROJECT SUMMARY/ABSTRACT
Tumor vascular networks are highly abnormal and complex, vary significantly by tumor type and on a patient-
by-patient basis. More importantly, patient-specific tumor vascular properties are known to regulate cancer
progression and treatment response in patients with primary or metastatic liver cancers. Non-invasive rapid
diagnostic methods to characterize unique tumor vascular properties in liver malignancies, and to provide
clinical decision-support, are currently not available. Our goal is to introduce computational approaches to
characterize complex attributes of vascular networks by considering the interconnected nature of
voxels in 3D dynamic contrast-enhanced ultrasound (3D DCE-US), and to validate these as biomarkers
for diagnostics or treatment monitoring. We have already contributed to pioneering 3D DCE-US liver
imaging in the clinic and have demonstrated its ability to minimize 2D-based sampling errors and improved
prediction of treatment response longitudinally. However, current quantification approaches are designed for
2D imaging and do not take into account vascular heterogeneity and the contrast flow field. In addition,
conventional parameters that are typically extracted from averaged intensities in large regions of interest fail to
account for spatial variations of perfusion common in complex tumor tissues. The exclusive volumetric nature
of our data along with the intravascular nature of ultrasound contrast agents, has allowed us to demonstrate
that additional information is encoded in spatial flow maps beyond convention, and that this information is
sensitive to treatment response and representative of the underlying true vascular network. Thus, our
hypothesis is that characterizing the interconnected nature of voxels in 3D DCE-US can capture new
information from volumetric tissues for cancer applications, and beyond cancer. Therefore, we propose a set of
specific aims designed to test this hypothesis by: i) further developing and ii) extensively validating new
perfusion measurements to characterize different tumor vascular properties and detect subtle microvascular
changes following therapy, and iii) clinically validating their clinical utility for diagnostics and treatment
monitoring in real patients imaged with 3D DCE-US. Our proposal takes advantage of the recent clinical
introduction of commercial ultrasound contrast agents and 3D imaging probes to advance non-invasive
bedside liver imaging for capturing complex flow beyond convention.

## Key facts

- **NIH application ID:** 10982172
- **Project number:** 1R01CA286505-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Ahmed El Kaffas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $599,191
- **Award type:** 1
- **Project period:** 2024-09-16 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10982172

## Citation

> US National Institutes of Health, RePORTER application 10982172, A 3D DCE-US Interconnected Voxel Analysis Framework for Bedside Characterization of Tumor Vascular Properties in Liver Malignancies. (1R01CA286505-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10982172. Licensed CC0.

---

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