Artificial Intelligence for Digital Cholangioscopy Neoplasia Diagnosis

Common Bile Duct Neoplasms Clinical Trial

Official title:

Clinical Validation of an Artificial Intelligence Software for Digital Cholangioscopy Diagnosis: an Observational Trial

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05147389
• Other study ID #: IECED-11032021
• Status: Completed
• Start date: October 1, 2020
• Est. completion date: May 1, 2022

Study information

• Verified date: November 2022
• Source: Instituto Ecuatoriano de Enfermedades Digestivas
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 170
• Est. completion date: May 1, 2022
• Est. primary completion date: November 30, 2021
• Gender: All
• Age group: 18 Years to 79 Years

Eligibility

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.

Related Conditions & MeSH terms

• Bile Duct Diseases
• Bile Duct Neoplasms
• Common Bile Duct Neoplasms

Intervention

Diagnostic Test:
• AI model classification
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.
• DSOC endoscopist experts' classification
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.

Locations

Country	Name	City	State
Belgium	Department of Advanced Interventional Endoscopy, Universitair Ziekenhuis Brussel (UZB)/Vrije Universiteit Brussel (VUB)	Brussels
Brazil	Serviço de Endoscopía Gastrointestinal do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo	São Paulo
Ecuador	Carlos Robles-Medranda	Guayaquil	Guayas
United States	Baylor Saint Luke's Medical Center	Houston	Texas
United States	Houston Methodist Hospital	Houston	Texas
United States	Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University	New Brunswick	New Jersey

Sponsors (7)

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

Countries where clinical trial is conducted

United States,  Belgium,  Brazil,  Ecuador, 

References & Publications (6)

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. — View 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. — View Citation

Robles-Medranda C, Oleas R, Sánchez-Carriel M, Olmos JI, Alcívar-Vásquez 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 cholang — View 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 — View 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. Gastroi — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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