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CC BY-NC-ND 4.0 · Endosc Int Open 2024; 12(10): E1118-E1126
DOI: 10.1055/a-2404-5699
Original article

Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures

Carlos Robles-Medranda 
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Jorge Baquerizo-Burgos
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Miguel Puga-Tejada
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Domenica Cunto
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Maria Egas-Izquierdo
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Juan Carlos Mendez
2   Research and Development, mdconsgroup, Guayaquil, Ecuador
,
Martha Arevalo-Mora 
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Juan Alcivar Vasquez
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Hannah Lukashok
1   Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas – IECED, Guayaquil, Ecuador
,
Daniela Tabacelia
3   Gastroenterology, Elias Emergency University Hospital, Bucuresti, Romania (Ringgold ID: RIN434252)
4   Universitatea de Medicină și Farmacie Carol Davila din București, Bucuresti, Romania (Ringgold ID: RIN87267)
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Abstract

Background and study aims Artificial intelligence (AI) models have demonstrated high diagnostic performance identifying neoplasia during digital single-operator cholangioscopy (DSOC). To date, there are no studies directly comparing AI vs. DSOC-guided probe-base confocal laser endomicroscopy (DSOC-pCLE). Thus, we aimed to compare the diagnostic accuracy of a DSOC-based AI model with DSOC-pCLE for identifying neoplasia in patients with indeterminate biliary strictures.

Patients and methods This retrospective cohort-based diagnostic accuracy study included patients ≥ 18 years old who underwent DSOC and DSOC-pCLE (June 2014 to May 2022). Four methods were used to diagnose each patient's biliary structure, including DSOC direct visualization, DSOC-pCLE, an offline DSOC-based AI model analysis performed in DSOC recordings, and DSOC/pCLE-guided biopsies. The reference standard for neoplasia was a diagnosis based on further clinical evolution, imaging, or surgical specimen findings during a 12-month follow-up period.

Results A total of 90 patients were included in the study. Eighty-six of 90 (95.5%) had neoplastic lesions including cholangiocarcinoma (98.8%) and tubulopapillary adenoma (1.2%). Four cases were inflammatory including two cases with chronic inflammation and two cases of primary sclerosing cholangitis. Compared with DSOC-AI, which obtained an area under the receiver operator curve (AUC) of 0.79, DSOC direct visualization had an AUC of 0.74 (P = 0.763), DSOC-pCLE had an AUC of 0.72 (P = 0.634), and DSOC- and pCLE-guided biopsy had an AUC of 0.83 (P = 0.809).

Conclusions The DSOC-AI model demonstrated an offline diagnostic performance similar to that of DSOC-pCLE, DSOC alone, and DSOC/pCLE-guided biopsies. Larger multicenter, prospective, head-to-head trials with a proportional sample among neoplastic and nonneoplastic cases are advisable to confirm the obtained results.

Keywords

Pancreatobiliary (ERCP/PTCD) - Cholangioscopy - Tissue diagnosis - Strictures


Publication History

Received: 19 January 2024

Accepted after revision: 24 July 2024

Article published online:
10 October 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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