An Innovative CFD-based Dosimetry and Pre-treatment Planning Platform to Support Personalized Transarterial Therapies for Liver Cancer
NCT ID: NCT07315633
Last Updated: 2026-01-02
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
80 participants
OBSERVATIONAL
2025-09-23
2030-12-31
Brief Summary
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The long-term goal would be to make these treatments more predictable and effective by developing a patient-specific pre-treatment planning platform. Blood flow and microsphere transport will be modelled in a digital model of the patient-specific hepatic arterial tree (based on clinical imaging) using computational fluid dynamics (CFD), in combination with Monte Carlo-based radiation dosimetry. Using CFD simulations, we will investigate how variations in treatment parameters influence the microsphere distribution, aiming to better understand their role in treatment variability. This will allow us to predict the dose distribution of a certain treatment and determine potentially a more optimal set of treatment parameters. This research contributes to the broader field of cancer research by laying the foundations for a digital tool for personalized pre-treatment planning. The insights gained could support interventional radiologists in optimizing treatment planning, improving tumor targeting, and minimizing radiation exposure to healthy liver tissue in future TARE procedures.
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Detailed Description
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A challenge in TARE is the limited ability to predict how the injected microspheres will spread throughout the arterial network of the liver. As a result, microspheres do not always deposit homogenously within the tumor, and part of the injected dose may end up in healthy liver tissue. This may reduce treatment efficacy while increasing the risk of complications. Current pre-treatment planning and dosimetry approaches are not completely able to predict the resulting radiation dose.
The goal of this study is to develop a methodology that enables better understanding and prediction of microsphere distribution and radiation dose during TARE using patient-specific 3D models. These models will be constructed from clinical imaging data such as CT, MRI, PET, SPECT, or DSA, and will be used to analyze hepatic arterial anatomy and dose deposition patterns. By studying these relationships, the project aims to identify which anatomical and procedural factors influence microsphere transport and dose delivery, and how these insights can contribute to improved pre-treatment planning.
The clinical part of the study consists solely of collecting retrospective and prospective imaging and treatment data from patients undergoing TARE, without altering clinical workflow. Therefore, no additional risk or discomfort is introduced for participating patients. The collected data will serve as the foundation for developing and validating the 3D models. In the long term, the resulting insights may help optimize catheter positioning, injection parameters, and dose planning, with the ambition of achieving more effective, personalized, and safer TARE treatments.
Conditions
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Study Design
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COHORT
OTHER
Study Groups
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HCC patients who undergo TARE
Endovascular treatment
Endovascular treatment
Interventions
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Endovascular treatment
Endovascular treatment
Eligibility Criteria
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Inclusion Criteria
* Subjects who are treated (or will be treated) using TARE with radioactive microspheres.
* Subjects for whom pre-treatment medical imaging of the hepatic arterial vasculature was performed for TARE planning (e.g., CT, MRI, PET, SPECT, and DSA), which can be used to develop patient-specific 3D models of the hepatic arterial tree and treatment dose distribution.
Exclusion Criteria
* Subjects in whom no pre- or post-treatment imaging data are available for analysis.
18 Years
ALL
No
Sponsors
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University Ghent
OTHER
University Hospital, Ghent
OTHER
Responsible Party
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Locations
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University Hospital Ghent
Ghent, , Belgium
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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ONZ-2022-0334
Identifier Type: -
Identifier Source: org_study_id
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