NCT06531200

Building of Prognosis Model for Patients With Cirrhosis Based on Sarcopenia Assessed by Deep Learning

Building of Prognosis Model for Patients With Cirrhosis Based on Sarcopenia in Assessment With the Technology of Deep Learning

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Peking University People's Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

The goal of this observational study is to develop and validate a fully automated imaging deep learning platform for the evaluation of sarcopenia in liver cirrhosis. Based on this model, a new prognostic model for liver cirrhosis incorporating imaging biomarkers such as sarcopenia will be constructed, and its predictive performance will be validated.

Detailed description

The goal of this observational study is to collect clinical and abdominal imaging data of patients with liver cirrhosis. The collected imaging data will be used as a model development set to develop, test, and internally validate a fully automated imaging deep learning platform for the evaluation of sarcopenia in liver cirrhosis. Subsequently, relevant data from patients with liver cirrhosis at other centers will be collected and used as an external validation dataset. The model will be externally validated by abdominal radiology experts. Furthermore, we will include sociodemographic information, clinical data, imaging data, and clinical outcomes of the aforementioned liver cirrhosis patients to predict the prognosis of these patients using the established model. This model will be used to construct a new prognostic model for liver cirrhosis incorporating imaging biomarkers such as sarcopenia, and its predictive performance will be validated.

Conditions

• Cirrhosis, Liver
• Sarcopenia

Timeline

• Start date: 2024-09-01
• Primary completion: 2025-08-31
• Completion: 2025-12-31
• First posted: 2024-07-31
• Last updated: 2024-08-06