Artificial Intelligence (AI) Cytopathology Trial
ConditionPancreatic Solid Lesions
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-100
SponsorThe University of Texas Health Science Center, Houston
About this trial
Purpose The primary objective of the study is to compare interpretation of EUS FNA/FNB samples for adequacy between ROSE and AI at bedside. To compare accuracy of preliminary diagnosis results between ROSE and AI at bedside versus final pathology report.
Research design This is a prospective single center study to compare performance characteristics in the interpretation of EUS FNA/FNB samples between AI and ROSE.
Procedures to be used Eligible patients will undergo EUS guided FNA/FNA of PSLs using standard of care. Sample slides are prepared by a cytopathologist at bedside and observed under a microscope. At the same time, the slides are scanned using a slide scanner and those images are saved for interpretation by AI at a later time.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Have EUS finding of a PSL;
Do not have contraindications for FNA/FNB.
Disqualifiers
Inability to provide informed consent for the procedure;
Contraindication for FNA/FNB eg coagulopathy, lack of avascular window for FNA.
Trial population
Patients will be selected from the inpatients and outpatients departments of the participating hospital and will be included in the study. A consent will be obtained for the EUS FNA/FNB per standard of care.
Trial design
Design model
Case-only
Time perspective
Prospective
Treatments tested in this trial
Artificial Intelligence software ROSE
Other interventionRapid on-site evaluation (ROSE) of Endoscopic Ultrasound (EUS) guided FNA/FNB (Fine Needle Aspirate/Fine Needle Biopsy) of pancreatic solid lesions (PSLs) has been shown in improve diagnostic yield. The availability and performance of ROSE at EUS performing centers is variable. With strides in Artificial Intelligence (AI) capabilities over the years, the University of Texas at Health Sciences Center at Houston in collaboration with Haystac is developing an artificial intelligence based proprietary system to analyze slides from EUS FNA/FNB samples at bedside.
Treatment groups
50 Participants
are divided into 1 treatment groupGroup A: Prospective enrollment1 intervention
Trial outcomes
Primary outcomes
1
Detection the adequacy for diagnosis
The primary outcome of the study is to determine how AI compares with ROSE in determining if EUS FNA/FNB sample from PSLs is adequate for diagnosis. This will be interpreted as a percentage in each group. The main study parameter is on-site determination if an EUS FNA/FNB sample is adequate for interpretation and diagnosis
Time frame
During procedureSecondary outcomes
1
Comparing the accuracy between preliminary diagnosis
To compare the accuracy between AI and ROSE preliminary diagnosis versus the final pathology report. Interpretation of preliminary results will be divided into categories of benign vs malignancy, acinar cells vs ductal cells in benign, adenocarcinoma vs neuroendocrine tumor vs other in malignancy.
Time frame
During procedureSponsors and contacts
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Source ClinicalTrials.gov