AI Guidance for Biopsy in Suspected Cholangiocarcinoma

NCT05374122 | Unknown DMC

• 1 other identifier: IECED-05052022
• Study Type: interventional
• Target: 48
• Locations: 1 country
• Sites: 1

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.

Conditions

Neoplasms; Non-Neoplastic; Bile Duct Neoplasms; Bile Duct Lesions

Keywords

Artificial Intelligence; Cholangioscopy; cholangiocarcinoma; biopsy

Outcome Measures

Primary Outcomes (1)

• 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.

  Six months

Secondary Outcomes (1)

• Insufficient biopsy sample rate

  Six months

Study Arms (2)

DSOC + AI-biopsy guidance

EXPERIMENTAL

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.

Diagnostic Test: DSOC with AI biopsy guidance

DSOC biopsy without AI guidance

ACTIVE COMPARATOR

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.

Diagnostic Test: DSOC biopsy without AI guidance

Interventions

DSOC with AI biopsy guidance 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 + AI-biopsy guidance

DSOC biopsy without AI guidance 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.

DSOC biopsy without AI guidance

Eligibility Criteria

• Age: 18 Years - 99 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Instituto Ecuatoriano de Enfermedades Digestivas: lead

Study Sites (1)

Carlos Robles-Medranda

Guayaquil, Guayas, 090505, Ecuador

Location

Related Publications (6)

• 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.

  PMID: 34508767 RESULT

• 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.

  PMID: 32707155 RESULT

• 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.

  PMID: 29954008 RESULT

• 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.

  PMID: 32185396 RESULT

• 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.

  PMID: 31778656 RESULT

• 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.

  PMID: 34704969 RESULT

MeSH Terms

Conditions

Neoplasms; Bile Duct Neoplasms; Cholangiocarcinoma

Condition Hierarchy (Ancestors)

Biliary Tract Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Bile Duct Diseases; Biliary Tract Diseases; Digestive System Diseases; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Study Officials

• Carlos Robles-Medranda, MD FASGE

  Ecuadorian Institute of Digestive Diseases

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: Randomized controlled trial

Study Record Dates

• First Submitted: May 5, 2022
• First Posted: May 16, 2022
• Study Start: May 1, 2022
• Primary Completion: December 30, 2023
• Study Completion: May 1, 2024
• Last Updated: March 6, 2023

Record last verified: 2023-03

Data Sharing

• IPD Sharing: Will not share

Locations

EC (1)