Deep Learning-Based Multidimensional Body Composition Mapping for Outcome Prediction in HCC Patients Undergoing TACE
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Hepatocellular Carcinoma. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Deep Learning-Based Multidimensional Body Composition Mapping for Predicting Clinical Outcomes in Hepatocellular Carcinoma Patients Undergoing TACE
Overview
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.
Primary outcome measures
- OS [Time frame: After the TACE procedure until May 1, 2025]
Secondary outcome measures (1)
- PFS [Time frame: After the TACE procedure until May 1, 2025]
Eligibility criteria
Inclusion criteria
- Patients diagnosed with "Hepatocellular Carcinoma" from January 1, 2018 to May 31, 2024;
- Age > 18 years old.
Exclusion criteria
- Poor image quality;
- Loss of follow-up;
- Presence of another type of malignant tumor other than liver cancer;
- Incomplete medical records.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan
Identifiers
NCT: NCT07235410 · [2025](1186)