Hepatocellular Carcinoma
Conditions
Brief 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.
Interventions
None listed
Sponsors
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
1. Patients diagnosed with Hepatocellular Carcinoma from January 1, 2018 to May 31, 2024; 2. Age \> 18 years old.
Exclusion criteria
1. Poor image quality; 2. Loss of follow-up; 3. Presence of another type of malignant tumor other than liver cancer; 4. Incomplete medical records.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| OS | After the TACE procedure until May 1, 2025 |
Secondary
| Measure | Time frame |
|---|---|
| PFS | After the TACE procedure until May 1, 2025 |
Countries
China
Contacts
Primary ContactYuanyuan Chu
Outcome results
None listed