Clinical trial NCT06824909: 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.
Official title: 内镜超声穿刺胰腺实性占位细胞涂片快速染色后全玻片扫描及人工智能诊断：一项前瞻性、多中心研究
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pancreatic Disease
Lead sponsor: Ruijin Hospital
Sponsor class: OTHER
Collaborators: name: Second Affiliated Hospital of Soochow University
name: Fudan University
name: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
name: The Third Xiangya Hospital of Central South University
name: Shanghai 10th People's Hospital
name: Affiliated Hospital of Jiangnan University
name: Jiangyin People's Hospital
First posted: 2025-02-13
Enrollment: 1500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: Inclusion Criteria:

* 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)

Exclusion Criteria:

* 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

Primary outcomes:
measure: Accuracy
timeFrame: through study completion, an average of 2 years
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
state: Shanghai
city: Shanghai
country: China
facility: Department of Gastroenterolog, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
state: Shanghai
Study officials:
References: