Clinical trial NCT05147389: Artificial Intelligence for Digital Cholangioscopy Neoplasia Diagnosis
Official title: Clinical Validation of an Artificial Intelligence Software for Digital Cholangioscopy Diagnosis: an Observational Trial
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Common Bile Duct Neoplasms
Non-Neoplastic Bile Duct Disorder
Keywords: Artificial Intelligence
Cholangioscopy
Neoplasia
Lead sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Sponsor class: OTHER
Collaborators: name: The Methodist Hospital Research Institute
name: University of Sao Paulo
name: Vrije Universiteit Brussel
name: Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University
name: Baylor St. Luke's Medical Center
name: Universitair Ziekenhuis Brussel
First posted: 2021-12-07
Enrollment: 170
Sex: ALL
Minimum age (years): 18
Maximum age (years): 79
Standard ages: ADULT
OLDER_ADULT
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients with indication of DSOC.

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.

Detailed description: Distinguishing neoplastic from non-neoplastic bile duct lesions is a challenge for clinicians. Magnetic resonance (MR) and biopsy guided by endoscopic retrograde cholangiopancreatography (ERCP) reached a negative predictive value (NPV) around 50%. On the other hand, Digital single-operator cholangioscopy (DSOC) findings achieve high diagnostic accuracy for neoplastic bile duct lesions. DSOC could be even better than DSOC-guided biopsy, which is inconclusive in some cases. However, to date, there is no universally accepted DSOC classification for that purpose. Also, endoscopists' Intra and interobserver agreements vary widely. Therefore, a more reproducible system is still needed.

With interesting results, Cholangiocarcinoma (CCA) assessment through artificial intelligence (AI) tools has been developed based on imaging radiomics. Nevertheless, CCA AI resources are almost exclusively for intrahepatic CCA (iCCA), with an endoscopic technique. Therefore, more AI tools are necessary for assessing extrahepatic neoplastic bile duct lesions. Perihilar (pCCA) and distal (dCCA) cholangiocarcinoma as the most typical neoplastic bile duct lesions. Both represent 50-60% and 20-30% of all CCA, including secondary malignancies by local extension (hepatocarcinoma, gallbladder, and pancreas cancer).

A recent Portuguese proof-of-concept study developed an AI tool based on convolutional neuronal networks (CNNs). It let to differentiate between malignant from benign bile duct lesions or normal tissue with very high accuracy. Still, it needs more external validation, including endoscopists' Intra and interobserver agreement comparison. In Ecuador, the investigators recently proposed an AI model to classify bile duct lesions during real-time DSOC, which has been able to detect malignancy pattern in all cases.

This research pursues a clinical validation of our AI model for distinguishing between neoplastic and non-neoplastic bile duct lesions, compared with six endoscopists with high DSOC experience.

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 recording DSOC procedure for this study.

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Neoplastic bile duct diagnosis confirmation after one year follow-up
timeFrame: One year
Secondary outcomes:
Other outcomes:
Locations:
city: New Brunswick
country: United States
facility: Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University
state: New Jersey
city: Houston
country: United States
facility: Baylor Saint Luke's Medical Center
state: Texas
city: Houston
country: United States
facility: Houston Methodist Hospital
state: Texas
city: Brussels
country: Belgium
facility: Department of Advanced Interventional Endoscopy, Universitair Ziekenhuis Brussel (UZB)/Vrije Universiteit Brussel (VUB)
city: São Paulo
country: Brazil
facility: Serviço de Endoscopía Gastrointestinal do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo
city: Guayaquil
country: Ecuador
facility: Carlos Robles-Medranda
state: Guayas
Study officials:
affiliation: Ecuadorian Institute of Digestive Diseases
name: Carlos Robles-Medranda
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
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.
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.
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.