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AI Guidance for Biopsy in Suspected Cholangiocarcinoma

Primary Purpose

Neoplasms, Non-Neoplastic, Bile Duct Neoplasms

Status
Active
Phase
Not Applicable
Locations
Ecuador
Study Type
Interventional
Intervention
DSOC with AI biopsy guidance
DSOC biopsy without AI guidance
Sponsored by
Instituto Ecuatoriano de Enfermedades Digestivas
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Neoplasms focused on measuring Artificial Intelligence, Cholangioscopy, cholangiocarcinoma, biopsy

Eligibility Criteria

18 Years - 99 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

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.

Exclusion Criteria:

  • Any clinical condition which makes DSOC inviable.
  • Patients with more than one DSOC.
  • Lost on a six-month follow-up after DSOC.

Sites / Locations

  • Carlos Robles-Medranda

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

DSOC + AI-biopsy guidance

DSOC biopsy without AI guidance

Arm Description

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.

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.

Outcomes

Primary Outcome Measures

Cholangiocarcinoma diagnosis confirmation after biopsy and six-month follow-up
To confirm the diagnosis based on pathology results from specimens obtained through DSOC (with or without AI-guided biopsy) or findings from further indicated procedures, including brush cytology fluoroscopy-guided biopsy, endoscopic ultrasound-guided tissue sampling, and surgical samples. Finally, the gold standard is a six-month follow-up compared against the AI model (group 1) or the DSOC endoscopist experts' classification. The data will be verified through a 2 x 2 contingency table.

Secondary Outcome Measures

Insufficient biopsy sample rate
Four biopsies will be performed per each case. Rate of insufficient samples by each study group will be recorded and compared.

Full Information

First Posted
May 5, 2022
Last Updated
March 3, 2023
Sponsor
Instituto Ecuatoriano de Enfermedades Digestivas
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1. Study Identification

Unique Protocol Identification Number
NCT05374122
Brief Title
AI Guidance for Biopsy in Suspected Cholangiocarcinoma
Official Title
Efficacy of Artificial Intelligence Aid-digital Single-operator Cholangioscopy (DSOC) Guided-biopsy Sampling in Suspected Cholangiocarcinoma: A Prospective, Randomized Trial
Study Type
Interventional

2. Study Status

Record Verification Date
March 2023
Overall Recruitment Status
Active, not recruiting
Study Start Date
May 1, 2022 (Actual)
Primary Completion Date
December 30, 2023 (Anticipated)
Study Completion Date
May 1, 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Instituto Ecuatoriano de Enfermedades Digestivas

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes

5. Study Description

Brief Summary
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.
Detailed Description
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Neoplasms, Non-Neoplastic, Bile Duct Neoplasms, Bile Duct Lesions
Keywords
Artificial Intelligence, Cholangioscopy, cholangiocarcinoma, biopsy

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Model Description
Randomized controlled trial
Masking
None (Open Label)
Allocation
Randomized
Enrollment
48 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
DSOC + AI-biopsy guidance
Arm Type
Experimental
Arm Description
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.
Arm Title
DSOC biopsy without AI guidance
Arm Type
Active Comparator
Arm Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
DSOC with AI biopsy guidance
Intervention Description
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
Intervention Name(s)
DSOC biopsy without AI guidance
Intervention Description
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.
Primary Outcome Measure Information:
Title
Cholangiocarcinoma diagnosis confirmation after biopsy and six-month follow-up
Description
To confirm the diagnosis based on pathology results from specimens obtained through DSOC (with or without AI-guided biopsy) or findings from further indicated procedures, including brush cytology fluoroscopy-guided biopsy, endoscopic ultrasound-guided tissue sampling, and surgical samples. Finally, the gold standard is a six-month follow-up compared against the AI model (group 1) or the DSOC endoscopist experts' classification. The data will be verified through a 2 x 2 contingency table.
Time Frame
Six months
Secondary Outcome Measure Information:
Title
Insufficient biopsy sample rate
Description
Four biopsies will be performed per each case. Rate of insufficient samples by each study group will be recorded and compared.
Time Frame
Six months

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
99 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
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. Exclusion Criteria: Any clinical condition which makes DSOC inviable. Patients with more than one DSOC. Lost on a six-month follow-up after DSOC.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Carlos Robles-Medranda, MD FASGE
Organizational Affiliation
Ecuadorian Institute of Digestive Diseases
Official's Role
Principal Investigator
Facility Information:
Facility Name
Carlos Robles-Medranda
City
Guayaquil
State/Province
Guayas
ZIP/Postal Code
090505
Country
Ecuador

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
34508767
Citation
Saraiva MM, Ribeiro T, Ferreira JPS, Boas FV, Afonso J, Santos AL, Parente MPL, Jorge RN, Pereira P, Macedo G. Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study. Gastrointest Endosc. 2022 Feb;95(2):339-348. doi: 10.1016/j.gie.2021.08.027. Epub 2021 Sep 8.
Results Reference
result
PubMed Identifier
32707155
Citation
Robles-Medranda C, Oleas R, Sanchez-Carriel M, Olmos JI, Alcivar-Vasquez J, Puga-Tejada M, Baquerizo-Burgos J, Icaza I, Pitanga-Lukashok H. Vascularity can distinguish neoplastic from non-neoplastic bile duct lesions during digital single-operator cholangioscopy. Gastrointest Endosc. 2021 Apr;93(4):935-941. doi: 10.1016/j.gie.2020.07.025. Epub 2020 Jul 22.
Results Reference
result
PubMed Identifier
29954008
Citation
Robles-Medranda C, Valero M, Soria-Alcivar M, Puga-Tejada M, Oleas R, Ospina-Arboleda J, Alvarado-Escobar H, Baquerizo-Burgos J, Robles-Jara C, Pitanga-Lukashok H. Reliability and accuracy of a novel classification system using peroral cholangioscopy for the diagnosis of bile duct lesions. Endoscopy. 2018 Nov;50(11):1059-1070. doi: 10.1055/a-0607-2534. Epub 2018 Jun 28.
Results Reference
result
PubMed Identifier
32185396
Citation
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020 Jan 1;2020:baaa010. doi: 10.1093/database/baaa010.
Results Reference
result
PubMed Identifier
31778656
Citation
Gerges C, Beyna T, Tang RSY, Bahin F, Lau JYW, van Geenen E, Neuhaus H, Nageshwar Reddy D, Ramchandani M. Digital single-operator peroral cholangioscopy-guided biopsy sampling versus ERCP-guided brushing for indeterminate biliary strictures: a prospective, randomized, multicenter trial (with video). Gastrointest Endosc. 2020 May;91(5):1105-1113. doi: 10.1016/j.gie.2019.11.025. Epub 2019 Nov 25.
Results Reference
result
PubMed Identifier
34704969
Citation
Ribeiro T, Saraiva MM, Afonso J, Ferreira JPS, Boas FV, Parente MPL, Jorge RN, Pereira P, Macedo G. Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy. Clin Transl Gastroenterol. 2021 Oct 27;12(11):e00418. doi: 10.14309/ctg.0000000000000418.
Results Reference
result

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AI Guidance for Biopsy in Suspected Cholangiocarcinoma

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