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Pancreatic tissue segmentation based on deep learning to build and validate a postoperative pancreatic fistula risk prediction model using machine learning

Pancreas tissue segmentation based on deep learning to build and validate a postoperative pancreatic fistula risk prediction model using machine learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600128528
Enrollment
Unknown
Registered
2026-07-22
Start date
2026-07-26
Completion date
Unknown
Last updated
2026-08-03

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

Conditions

Postoperative pancreatic fistula, POPF

Interventions

Observation group:None

Sponsors

First Affiliated Hospital of Kunming Medical University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1.Postoperative pathology confirmed it as a pancreatic tumor, and surgery was performed. Within 14 days before the surgery, a standard contrast-enhanced CT scan was done.

Exclusion criteria

Exclusion criteria: 1.Cases with other malignant tumors, missing follow-up data making it impossible to determine outcomes, and missing outcome variable information.

Design outcomes

Primary

MeasureTime frame
Postoperative pancreatic fistula, POPF;—

Secondary

MeasureTime frame
Pancreas fat content;Pancreatic duct size;—

Countries

China

Contacts

Public ContactLi Jiang

First Affiliated Hospital of Kunming Medical University

13888836057@163.com+86 871 65324888

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Aug 10, 2026