Deep Learning-Based Multidimensional Body Composition Mapping for Outcome Prediction in HCC Patients Undergoing TACE

Brief summary (registry, in English)

Hepatocellular carcinoma (HCC) is a common liver cancer, and many patients cannot receive surgery. For these patients, transarterial chemoembolization (TACE) is an important treatment. However, patients often respond differently to TACE, and it is difficult to predict who will benefit most. This study uses deep learning to automatically analyze routine CT images taken before TACE. By measuring body composition features, such as the size and condition of different abdominal organs and tissues, we aim to better understand patients' overall health status and treatment tolerance. The goal is to develop a prediction model that can help doctors estimate survival and treatment outcomes more accurately. This may assist in making more personalized treatment decisions and improving patient care.

What you commit to

• Study type: Observational
• Planned participants: 300
• From 1 Nov 2025 to the primary completion date, 31 Dec 2025
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 18 years to - · Sex: All

Full criteria on ClinicalTrials.gov

Sites (1)

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