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

Recruiting now

Conditions studied: Hepatocellular Carcinoma

In brief

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.

Key facts

Study ID

NCT07235410

Run by

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

People needed

300

Starts

2025-11-01

Expected to finish

2026-11-01

Last updated by the study team

2025-11-19

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Patients diagnosed with "Hepatocellular Carcinoma" from January 1, 2018 to May 31, 2024;
• Age > 18 years old.

You may not qualify if…

• Poor image quality;
• Loss of follow-up;
• Presence of another type of malignant tumor other than liver cancer;
• Incomplete medical records.

Where it is running

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan, Hubei, China (enrolling)

Full record on ClinicalTrials.gov

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