Endoscopic Ultrasound-guided Fine-needle Aspiration of Solid Pancreatic Lesions With Rapid Staining of Cytological Smears Followed by Whole Slide Scanning and Artificial Intelligence Diagnosis: A Prospective, Multicenter Study.

NCT06824909 | Recruiting

• 1 other identifier: RuijinH2024574
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 2

Brief Summary

The objective of this observational study is to investigate whether the self-developed whole slide scanning and artificial intelligence diagnostic system for pancreatic solid lesion puncture cytopathology (hereinafter referred to as the "Zhiying Shunxi" ROSE-AI diagnostic system) can promptly and accurately diagnose solid pancreatic lesions (SPLs). The main question it aims to answer is: By utilizing optical imaging technology to capture RGB images of Diff-Quik stained smears from pancreatic punctures, can the development of artificial intelligence algorithms assist in differentiating solid pancreatic space-occupying diseases (such as pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumors, and non-neoplastic benign lesions)? Researchers will compare the diagnoses of SPLs made by the ROSE-AI system with the actual pathological diagnoses of the SPLs themselves to determine whether the ROSE-AI system can effectively diagnose SPLs.

Conditions

Pancreatic Disease

Outcome Measures

Primary Outcomes (1)

• Accuracy

  Accuracy = (TP + TN) / (TP + FP + FN + TN)

  through study completion, an average of 2 years

Study Arms (3)

pancreatic ductal adenocarcinoma

Device: ROSE-AI diagnostic system

pancreatic neuroendocrine tumor

Device: ROSE-AI diagnostic system

non-neoplastic benign lesions

Device: ROSE-AI diagnostic system

Interventions

ROSE-AI diagnostic system DEVICE

All samples were obtained due to the necessity for disease treatment and in accordance with routine clinical workflows. After the pathological diagnoses were confirmed by the pathology departments of the hospitals affiliated with the respective endoscopic centers, the eligible pancreatic puncture Diff-Quik stained smears were borrowed and transferred to Ruijin Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University. There, the self-developed "Zhiying Shunxi" system was used to capture corresponding traditional light microscope RGB images. After the imaging was completed, all specimens were returned to the endoscopic centers from which they originated. Using the RGB images as input, an artificial intelligence algorithm was developed to assist in differentiating solid pancreatic lesions.

non-neoplastic benign lesions; pancreatic ductal adenocarcinoma; pancreatic neuroendocrine tumor

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

All patients were obtained due to the necessity for disease treatment and in accordance with routine clinical workflows, and finally were confirmed as pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumors, and non-neoplastic benign lesions.

You may qualify if:

• A dated and signed informed consent form A commitment to abide by the research procedures and cooperate throughout the entire study Subjects aged 18 and above, regardless of gender Diagnosis or suspicion of a solid pancreatic space-occupying lesion based on imaging studies (B-mode ultrasound, CT, or MRI)

You may not qualify if:

• Unable or refusing to sign the informed consent form Unable to suspend anticoagulation/antiplatelet therapy Pregnant or lactating Having a mental illness or other medical conditions that are unsuitable for undergoing FNA/B biopsy Presence of coagulation disorders (PLT \< 50 × 10\^3/μl, INR \> 1.5) Pancreatic cystic lesions Non-diagnostic EUS-FNA/B specimens Having less than 8 microscopic fields of interest (ROI) in the digital pathology images of the entire Diff-Quik smear slide

Sponsors & Collaborators

• Ruijin Hospital: lead
• Second Affiliated Hospital of Soochow University: collaborator
• Fudan University: collaborator
• Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine: collaborator
• The Third Xiangya Hospital of Central South University: collaborator
• Shanghai 10th People's Hospital: collaborator
• Affiliated Hospital of Jiangnan University: collaborator
• Jiangyin People's Hospital: collaborator

Study Sites (2)

Ruijin Hospital, Shanghai Jiaotong University School of Medicine

Shanghai, Shanghai Municipality, 200000, China

RECRUITING

Department of Gastroenterolog, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine

Shanghai, Shanghai Municipality, 200025, China

RECRUITING

MeSH Terms

Conditions

Pancreatic Diseases

Condition Hierarchy (Ancestors)

Digestive System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 22, 2025
• First Posted: February 13, 2025
• Study Start: December 31, 2024
• Primary Completion (Estimated): May 31, 2027
• Study Completion (Estimated): June 30, 2027
• Last Updated: February 13, 2025

Record last verified: 2025-01

Locations

CN (2)