Artificial Intelligence for Digital Cholangioscopy Neoplasia Diagnosis

NCT05147389 | Completed DMC

• 1 other identifier: IECED-11032021
• Study Type: observational
• Target: 170
• Locations: 4 countries
• Sites: 6

Brief Summary

Digital single-operator cholangioscopy (DSOC) findings achieve high diagnostic accuracy for neoplastic bile duct lesions. To date, there is not a universally accepted DSOC classification. Endoscopists' Intra and interobserver agreements vary widely. Cholangiocarcinoma (CCA) assessment through artificial intelligence (AI) tools is almost exclusively for intrahepatic CCA (iCCA). Therefore, more AI tools are necessary for assessing extrahepatic neoplastic bile duct lesions. In Ecuador, the investigators have recently proposed an AI model to classify bile duct lesions during real-time DSOC, which accurately detected malignancy patterns. This research pursues a clinical validation of our AI model for distinguishing between neoplastic and non-neoplastic bile duct lesions, compared with high DSOC experienced endoscopists.

Conditions

Common Bile Duct Neoplasms; Non-Neoplastic Bile Duct Disorder

Keywords

Artificial Intelligence; Cholangioscopy; Neoplasia

Outcome Measures

Primary Outcomes (1)

• Neoplastic bile duct diagnosis confirmation after one year follow-up

  Cases will be first followed up during one year to confirm or discard neoplastic bile duct lesions. A definite diagnosis of neoplastic bile duct lesion will be based on DSOC-guided biopsy specimen or findings from further indicated procedures, including brush cytology fluoroscopy-guided, endoscopic ultrasound-guided tissue sampling, surgical samples, and even imaging test in the context of a more impaired patient. Finally, the agreement between one-year follow-up (gold standard) vs. AI model and DSOC endoscopist experts' classification will be verified through a 2 x 2 contingency table.

  One year

Study Arms (2)

Neoplastic bile duct lesions

This group is confirmed by DSOC videos from patients with DSOC-confirmed neoplastic bile duct lesions, coming from each participating group. Each DSOC video corresponds to a complete DSOC procedure in a single patient. The neoplastic bile duct criteria are in accordance with the two following tools: the Robles-Medranda et al and the Mendoza classification. A further follow will be necessary to confirm neoplastic bile duct lesion and the type: pCCA or dCCA, local extension of iCCA, hepatocarcinoma mixed CCA/hepatocarcinoma, gallbladder cancer, pancreas cancer, or any other neoplastic bile duct lesion. Based on follow-up, videos from patients with confirmed non-neoplastic bile duct lesions will be re-assessed and re-classified or finally excluded by an expert blinded to clinical records and who do not participate in videos classification.

Diagnostic Test: AI model classification; Diagnostic Test: DSOC endoscopist experts' classification

Non-neoplastic bile duct lesions

This group is confirmed by DSOC videos from patients with DSOC-confirmed non-neoplastic bile duct lesions, coming from each participating group. Each DSOC video corresponds to a complete DSOC procedure in a single patient. The non-neoplastic bile duct criteria are in accordance with the two following tools: the Robles-Medranda et al and the Mendoza classification. A further follow will be necessary to confirm non-neoplastic bile duct lesion and the type, when available: acute or chronic cholangitis secondary to stones or parasite's location, autoimmune cholestatic liver diseases as autoimmune sclerosant cholangitis, and primary biliary cholangitis. Based on follow-up, videos from patients with confirmed neoplastic bile duct lesions will be re-assessed and re-classified or finally excluded by an expert blinded to clinical records and who do not participate in videos classification.

Diagnostic Test: AI model classification; Diagnostic Test: DSOC endoscopist experts' classification

Interventions

AI model classification DIAGNOSTIC_TEST

AIWorks is an artificial intelligence model for real-time cholangioscopic detection of neoplastic and non-neoplastic bile duct lesions. It allows you to choose using a video file or a USB camera input as the detection source. Once the input source has been selected, the software performs real-time detection by surrounding the area of interest (i.e., the area with malignancy features) inside a bounding box. All detections made are displayed on the right side of the screen and can also be reviewed afterwards.

Neoplastic bile duct lesions; Non-neoplastic bile duct lesions

DSOC endoscopist experts' classification DIAGNOSTIC_TEST

Six endoscopists with high DSOC expertise will observe and classify a set of videos among neoplastic or non-neoplastic bile duct lesions following a Bernoulli distribution; blinded to clinical records and should have never attended said patients. Gastroenterologists from each center, with non-DSOC responsibility, will select DSOC videos and corresponding baseline data. DSOC videos and data will be gathered in one set. Each video represents a full DSOC for a single patient. The patient will be the unit of this study. The neoplastic bile duct criteria are in accordance with the Robles-Medranda et al and the Mendoza classifications (ie. Irregular mucosa surface, Tortuous and dilated vascularity, Irregular nodulations, Polyps, Ulceration, Honeycomb pattern, etc.). The experts will assess neoplastic bile duct by presence or absence of disaggregated criteria. Likewise, by Boolean logical operators, the statistical software will compute disaggregated answers.

Neoplastic bile duct lesions; Non-neoplastic bile duct lesions

Eligibility Criteria

• Age: 18 Years - 79 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients with indication of DSOC.

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 recording DSOC procedure for this study.

You may not qualify if:

• Any clinical condition which makes DSOC inviable.
• Patients with more than one DSOC.
• Low quality of recorded DSOC videos, even for AI model as for the expert endoscopists.
• Lost on a one-year follow-up after DSOC.
• Disagreement between DSOC findings vs. one-year follow-up, even after re-assessment of respective DSOC videos.

Sponsors & Collaborators

• Instituto Ecuatoriano de Enfermedades Digestivas: lead
• The Methodist Hospital Research Institute: collaborator
• University of Sao Paulo: collaborator
• Vrije Universiteit Brussel: collaborator
• Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University: collaborator
• Baylor St. Luke's Medical Center: collaborator
• Universitair Ziekenhuis Brussel: collaborator

Study Sites (6)

Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University

New Brunswick, New Jersey, 08901, United States

Location

Baylor Saint Luke's Medical Center

Houston, Texas, 77030, United States

Location

Houston Methodist Hospital

Houston, Texas, 77098, United States

Location

Department of Advanced Interventional Endoscopy, Universitair Ziekenhuis Brussel (UZB)/Vrije Universiteit Brussel (VUB)

Brussels, Belgium

Location

Serviço de Endoscopía Gastrointestinal do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo

São Paulo, Brazil

Location

Carlos Robles-Medranda

Guayaquil, Guayas, 090505, Ecuador

Location

Related Publications (7)

• Kahaleh M, Gaidhane M, Shahid HM, Tyberg A, Sarkar A, Ardengh JC, Kedia P, Andalib I, Gress F, Sethi A, Gan SI, Suresh S, Makar M, Bareket R, Slivka A, Widmer JL, Jamidar PA, Alkhiari R, Oleas R, Kim D, Robles-Medranda CA, Raijman I. Digital single-operator cholangioscopy interobserver study using a new classification: the Mendoza Classification (with video). Gastrointest Endosc. 2022 Feb;95(2):319-326. doi: 10.1016/j.gie.2021.08.015. Epub 2021 Aug 31.

  PMID: 34478737 BACKGROUND

• Sethi A, Tyberg A, Slivka A, Adler DG, Desai AP, Sejpal DV, Pleskow DK, Bertani H, Gan SI, Shah R, Arnelo U, Tarnasky PR, Banerjee S, Itoi T, Moon JH, Kim DC, Gaidhane M, Raijman I, Peterson BT, Gress FG, Kahaleh M. Digital Single-operator Cholangioscopy (DSOC) Improves Interobserver Agreement (IOA) and Accuracy for Evaluation of Indeterminate Biliary Strictures: The Monaco Classification. J Clin Gastroenterol. 2022 Feb 1;56(2):e94-e97. doi: 10.1097/MCG.0000000000001321.

  PMID: 32040050 BACKGROUND

• Kahaleh M, Raijman I, Gaidhane M, Tyberg A, Sethi A, Slivka A, Adler DG, Sejpal D, Shahid H, Sarkar A, Martins F, Boumitri C, Burton S, Bertani H, Tarnasky P, Gress F, Gan I, Ardengh JC, Kedia P, Arnelo U, Jamidar P, Shah RJ, Robles-Medranda C. Digital Cholangioscopic Interpretation: When North Meets the South. Dig Dis Sci. 2022 Apr;67(4):1345-1351. doi: 10.1007/s10620-021-06961-z. Epub 2021 Mar 30.

  PMID: 33783691 BACKGROUND

• 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

• Robles-Medranda C, Baquerizo-Burgos J, Alcivar-Vasquez J, Kahaleh M, Raijman I, Kunda R, Puga-Tejada M, Egas-Izquierdo M, Arevalo-Mora M, Mendez JC, Tyberg A, Sarkar A, Shahid H, Del Valle-Zavala R, Rodriguez J, Merfea RC, Barreto-Perez J, Saldana-Pazmino G, Calle-Loffredo D, Alvarado H, Lukashok HP. Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model. Endoscopy. 2023 Aug;55(8):719-727. doi: 10.1055/a-2034-3803. Epub 2023 Feb 13.

  PMID: 36781156 DERIVED

MeSH Terms

Conditions

Common Bile Duct Neoplasms; Neoplasms

Condition Hierarchy (Ancestors)

Bile Duct Neoplasms; Biliary Tract Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Bile Duct Diseases; Biliary Tract Diseases; Digestive System Diseases; Common Bile Duct Diseases

Study Officials

• Carlos Robles-Medranda

  Ecuadorian Institute of Digestive Diseases

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 5, 2021
• First Posted: December 7, 2021
• Study Start: October 1, 2020
• Primary Completion: November 30, 2021
• Study Completion: May 1, 2022
• Last Updated: November 17, 2022

Record last verified: 2022-11

Locations

BR (1); BE (1); US (3); EC (1)