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Radiomics-Driven Multimodal Deep Learning Model for Prediction of Refractory Hypersplenism after Liver Transplantation: A Single-Center Study

Radiomics-Driven Multimodal Deep Learning Model for Prediction of Refractory Hypersplenism after Liver Transplantation: A Single-Center Study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600121618
Enrollment
Unknown
Registered
2026-04-01
Start date
2026-04-01
Completion date
Unknown
Last updated
2026-04-14

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

Conditions

Liver transplantation

Interventions

Sponsors

Beijing Chaoyang Hospital, Capital Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 75 Years

Inclusion criteria

Inclusion criteria: 1. Age 18-75 years 2. Patients who undergo liver transplantation in our department; 3. No surgical contraindications identified in preoperative evaluation; 4. Preoperative evaluation indicates hypersplenism (meeting the following conditions simultaneously): i. Peripheral blood cell reduction (meeting any of the following): PLT 13 cm or spleen volume >350-400 mL; 5. Complete clinical, imaging, and follow-up data of the patient.

Exclusion criteria

Exclusion criteria: 1. Splenectomy or splenic artery embolization performed before transplantation; 2. Ligation of the splenic artery during surgery or splenic artery embolization performed postoperatively; 3. The patient has undergone TIPS or other surgical treatments to reduce portal pressure; 4. Perioperative death; 5. Complicated with hematologic diseases; 6. Others: such as major ethical issues or lack of informed consent;

Design outcomes

Primary

MeasureTime frame
Incidence of refractory hypersplenism at 6 months post-transplantation;

Secondary

MeasureTime frame
Indicators related to the degree of splenic function recovery;Clinical outcomes associated with hypersplenism;

Countries

China

Contacts

Public ContactLang Ren

Beijing Chaoyang Hospital, Capital Medical University

dr_langren@126.com+86 139 1175 7869

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Apr 17, 2026