Clinical trial · Interventional
AI Guidance for Biopsy in Suspected Cholangiocarcinoma
Efficacy of Artificial Intelligence Aid-digital Single-operator Cholangioscopy (DSOC) Guided-biopsy Sampling in Suspected Cholangiocarcinoma: A Prospective, Randomized Trial
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Bile Duct Lesions | — | UNRESOLVED | — |
| Bile Duct Neoplasms | Bile Duct Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Neoplasms | Neoplasm | ONTOLOGY_EXACT | 0.90 |
| Non-Neoplastic | Cholangiocarcinoma | PROBABILISTIC | 0.70 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| DSOC biopsy without AI guidance | Diagnostic Test | — | UNRESOLVED |
| DSOC with AI biopsy guidance | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- DSOC + AI-biopsy guidance
- 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.
- interventionNames
- Diagnostic Test: DSOC with AI biopsy guidance
- type
- ACTIVE_COMPARATOR
- label
- DSOC biopsy without AI guidance
- 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.
- interventionNames
- Diagnostic Test: DSOC biopsy without AI guidance
Primary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 99 Years
Show eligibility criteria text
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.
References
Publications (6)
- RESULTSaraiva 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
- RESULTRobles-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
- RESULTRobles-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
- RESULTAhmed 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
- RESULTGerges 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
- RESULTRibeiro 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.