AI Guidance for Biopsy in Suspected Cholangiocarcinoma

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

48 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-05-01

Study Completion Date

2024-05-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Digital single-operator cholangioscopy (DSOC) has emerged as a medical advance with an important role in the evaluation of indeterminate biliary lesions. This technique has demonstrated higher sensitivity in the guidance for tissue acquisition when compared with standard endoscopic retrograde cholangiopancreatography (ERCP). DSOC-guided biopsy is considered technically safe and successful for tissue collection.

Hand in hand with the development of more precise diagnostic techniques, comes the implementation of artificial intelligence (AI) for diagnostic assessment. For the past decade, the role of artificial intelligence (AI) has been increasing at a rapid pace. In the biliary tract, different models have been proposed for the characterization of malignant features. Nevertheless, to date, the discrepancy between the visual impression of the operator and the histological results obtained by cholangioscopy still present, affecting the accuracy the diagnosis.

Based on the above, the investigators aim to assess the diagnostic accuracy of AI for the guidance of tissue acquisition with DSOC compared to DSOC without AI for suspected cholangiocarcinoma. As a secondary aim, the investigators pursue to compare quality of AI-guided biopsies samples vs. DSOC biopsies without AI.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Real-Time Cholangioscopy Artificial Intelligence Evaluation of Biliary Strictures

NCT05779436

Biliary Stricture

RECRUITING

Primary EUS-GBD in Patients With Unresectable Malignant Biliary Obstruction and Cystic Duct Orifice Involvement.

NCT03729882

Acute Cholecystitis

COMPLETED NA

Digital Single-operator Cholangioscopy and Intraductal Ultrasound for Nonicteric Biliary Strictures

NCT06369896

Cholangiocarcinoma Biliary Stricture

NOT_YET_RECRUITING

Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety

NCT04719117

Gastrointestinal Disease Endoscopy Artificial Intelligence +1 more

UNKNOWN

Comparison of On-Site Versus Off-Site Evaluation of Cholangioscopy-Guided Biopsies of the Bile Duct

NCT01815619

Bile Duct Stricture

COMPLETED NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

The diagnosis and management of biliary malignancy currently represents a medical challenge. To date, DSOC has demonstrated high sensitivity in the detection of malignant biliary lesions, nevertheless there is not a universal expert consensus for the characterization of this lesions. Also, DSOC has shown to be safe and successful for specimen collection with higher sensitivity when compared with standard ERCP.

Moreover, most of the AI models proposed for characterization of neoplastic features in biliary lesions have demonstrated high reliability during DSOC performance. A model was the proposed by investigators in Ecuador, focused on the identification of features of malignancy. The detection is performed by surrounding the suspected lesion in a bounding box. The detected area is displayed in the right side of the screen. Also, the box/image of the presumptive lesion can also be recorded and reviewed afterwards. After the AI model detects the "malignant area", a tissue sample is collected and taken for histopathological studies.

In addition, due to a variation of the endoscopists´ intra and interobserver agreement and the discrepancy between the visual impression and histopathological findings, the investigators intend to take advantage of our AI model as a diagnostic tool for a more precise acquisition of tissue in lesions suggestive of malignancy during real-time DSOC.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Neoplasms Non-Neoplastic Bile Duct Neoplasms Bile Duct Lesions

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Randomized controlled trial

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

DSOC + AI-biopsy guidance

This group is comprised by patients with suggestive malignant biliary lesions assessed by DSOC for biopsy. In this group, the investigators aim to use as a complement tool an AI model for the detection of features suggestive of malignancy to perform the biopsy on the detecting bounding box signal.

A further follow-up of 6 months is necessary for a confirming diagnosis of neoplastic lesions.

Group Type EXPERIMENTAL

DSOC with AI biopsy guidance

Intervention Type DIAGNOSTIC_TEST

Patients with a presumptive diagnosis of biliary malignancy will undergo DSOC + Artificial intelligence model (AIWorks) guidance for detection of neoplastic lesion during real-time procedure, tissue sampling acquisition, and histopathological analysis.

DSOC biopsy without AI guidance

This group is comprised by patients with suggestive malignant biliary lesions assessed by DSOC for biopsy without AI guidance. A further follow-up of 6 months is necessary for a confirming diagnosis of neoplastic lesions.

Group Type ACTIVE_COMPARATOR

DSOC biopsy without AI guidance

Intervention Type DIAGNOSTIC_TEST

Patients with lesions suggestive of malignancy will undergo DSOC without AI guidance for sampling.

Based on the observer´s criteria regarding areas suggestive of malignancy, the collected tissue sample will be sent for histopathological studies.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

DSOC with AI biopsy guidance

Patients with a presumptive diagnosis of biliary malignancy will undergo DSOC + Artificial intelligence model (AIWorks) guidance for detection of neoplastic lesion during real-time procedure, tissue sampling acquisition, and histopathological analysis.

Intervention Type DIAGNOSTIC_TEST

DSOC biopsy without AI guidance

Patients with lesions suggestive of malignancy will undergo DSOC without AI guidance for sampling.

Based on the observer´s criteria regarding areas suggestive of malignancy, the collected tissue sample will be sent for histopathological studies.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Patients referred to our center with an indication of DSOC due to suspicion of CBD tumor or indeterminate CBD stenosis.
* Patients who authorized for DSOC-guided biopsy.