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

NCT07235410 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2025-11-19

No results posted yet for this study

Summary

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.

Conditions

Sponsors & Collaborators

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

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-11-01
Primary Completion
2025-12-31
Completion
2026-11-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07235410 on ClinicalTrials.gov