# A liver digital twin for personalized cancer therapy

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $357,939

## Abstract

Project Summary/ Abstract
Our long-term objective is to improve the efficacy and safety of liver cancer transarterial embolization through
new computational tools that enable the development of new personalized treatment strategies. The
continued rising mortality and incidence make research on improving liver cancer management essential.
Transarterial embolization is used to obstruct the tumor blood flow (TAE) and deliver localized radiation
(yttrium-90 radioembolization 90Y TARE) or chemotherapy (chemoembolization TACE). 90Y TARE counted
for more than 10,000 interventions in the US in 2022. Demonstrated benefits for patients include increased
time to progression but moderate improvement of overall survival, in part because it is only used as second
or third line treatment on advanced cancers. Recent 90Y TARE clinical trials showed a correlation between
the tumor dose and patient outcome, indicating that robust and precise targeting must be pursued. Targeting
is however complex, highly patient-dependent, and difficult to plan with current imaging techniques. This
leads physicians to underdose 90Y TARE to limit liver toxicity, missing the tumoricidal dose of ~50 in 80% of
patients. TAE and TACE are performed with a fixed dosage and also frequently fail: post treatment imaging
shows residual blood flow in ~70% of tumors treated with TACE, indicative of incomplete occlusion of the
tumor blood supply. If the efficacy and safety profile of TAE were improved through better planning, it could
have a much higher impact on patient outcome, helping patients at earlier stages and reducing mortality.
Tools to develop such treatment planning currently lack robustness and accuracy. This U01 proposal follows
the concept of a liver digital twin to develop an in silico platform to optimize liver transarterial embolization.
Tumor targeting is achieved by selecting the injection points and dosage; it remains mostly empirical based
on pretreatment vascular imaging with limited robustness. We propose a novel personalized treatment
planning using a liver digital twin that builds on our previous work developing CFDose, a simulation pipeline
based on computational fluid dynamics and physics modeling informed with patient CT images. CFDose
predicts the liver dose through blood flow simulation using standard-of-care imaging, requiring no changes
to the clinical workflow. We will use it as a building block to develop patient-specific in silico optimization of
TAE, TARE, and TACE. The algorithm will sample the injection point and dosage, simulate the dose or drug
concentration distribution (activating the liver model multiple times), and compare it with the physician’s
target. The project will develop the patient-specific virtual liver model to simulate the distribution, will
accelerate the simulation with artificial intelligence (GANs), and will integrate the liver model into an
optimization algorithm. The virtual liver model acts a digital twin of the patient’s liver to a...

## Key facts

- **NIH application ID:** 10992569
- **Project number:** 1U01CA289068-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Emilie Roncali
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $357,939
- **Award type:** 1
- **Project period:** 2024-09-17 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10992569

## Citation

> US National Institutes of Health, RePORTER application 10992569, A liver digital twin for personalized cancer therapy (1U01CA289068-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10992569. Licensed CC0.

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