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A study using deep learning radiomics nomograms based on medical images to predict CK-19 expression and prognosis in hepatocellular carcinoma patients who have undergone liver transplantation

A study using deep learning radiomics nomograms based on medical images to predict CK-19 expression and prognosis in hepatocellular carcinoma patients who have undergone liver transplantation.

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500111795
Enrollment
Unknown
Registered
2025-11-06
Start date
2024-08-23
Completion date
Unknown
Last updated
2025-11-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Liver transplantation

Interventions

Observation group of patients with hepatocellular carcinoma:None

Sponsors

Xiang’an Hospital of Xiamen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1.Age: 18-70 years old; 2. Patients who have been pathologically confirmed to have HCC after LT; 3. Patients who received enhanced CT scans within 15 days before LT; 4. The CT image quality is good and can be used for analysis. 5. There are complete clinical pathological and follow-up data

Exclusion criteria

Exclusion criteria: 1.There are other pathological types, such as intrahepatic cholangiocarcinoma (ICC) or mixed hepatocellular cholangiocarcinoma (CHC); 2. Lack of qualified CT images; 3. Lack of clinical information; 4. Death or disease recurrence within one month after liver transplantation

Design outcomes

Primary

MeasureTime frame
Radiomics and deep learning features of liver CT images;—

Countries

China

Contacts

Public ContactKe Ren

Xiang’an Hospital of Xiamen University

renke815@sina.com+86 592 288 9000

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026