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Constructing a Multimodal Artificial Intelligence Model Based on Endoscopic Ultrasound for the Diagnosis of Pancreatic Mass

Constructing a Multimodal Artificial Intelligence Model Based on Endoscopic Ultrasound for the Diagnosis of Pancreatic Mass

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400087297
Enrollment
Unknown
Registered
2024-07-24
Start date
2024-08-01
Completion date
Unknown
Last updated
2024-07-29

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

Conditions

Pancreatic mass

Interventions

Gold Standard:follow-up or pathology

Sponsors

Fudan University Shanghai Cancer Center
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1.Age > 18 years old, both males and females are eligible; 2.Patients who underwent radial EUS examination and had pancreatic space-occupying lesions detected between January 2012 and April 2024; 3.Have complete EUS scanning images or electronic video files; 4.Further biopsies (EUS, CT, or intraoperative) or surgeries were conducted to obtain a definitive pathological diagnosis; 5.For suspected benign diseases, follow-up for more than 1 year is required, with the lesion showing regression or no progression, and no clinical evidence of malignancy; 6.Pathological diagnoses include, but are not limited to, the following: pancreatic ductal adenocarcinoma, inflammatory pancreatic mass, pancreatic neuroendocrine tumor, solid pseudopapillary tumor of the pancreas, intraductal papillary mucinous neoplasm of the pancreas, mucinous cystic tumor of the pancreas, serous cystadenoma of the pancreas, metastatic cancer to the pancreas, ectopic spleen in the pancreas, etc.

Exclusion criteria

Exclusion criteria: Unable to provide clear images for the identification of the lesion area according to the standard.

Design outcomes

Primary

MeasureTime frame
Sensitivity;specificity;accuracy;AUC;

Countries

China

Contacts

Public ContactYu Xianjun

Fudan University Shanghai Cancer Center

yuxianjun@fudan.edu.cn+86 21 6417 5590

Outcome results

None listed

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