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

Autor:	Robles-Medranda C; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Baquerizo-Burgos J; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Puga-Tejada M; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Cunto D; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Egas-Izquierdo M; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Mendez JC; Research and Development, mdconsgroup, Guayaquil, Ecuador., Arevalo-Mora M; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Alcivar Vasquez J; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Lukashok H; Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador., Tabacelia D; Gastroenterology, Elias Emergency University Hospital, Bucuresti, Romania.; Universitatea de Medicină și Farmacie Carol Davila din București, Bucuresti, Romania.
Jazyk:	angličtina
Zdroj:	Endoscopy international open [Endosc Int Open] 2024 Oct 10; Vol. 12 (10), pp. E1118-E1126. Date of Electronic Publication: 2024 Oct 10 (Print Publication: 2024).
DOI:	10.1055/a-2404-5699
Abstrakt:	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. Competing Interests: Conflict of Interest Carlos Robles-Medranda is a key opinion leader and consultant for Pentax Medical, Steris, Medtronic, Motus, Micro-tech, G-Tech Medical Supply, CREO Medical, EndoSound, and mdconsgroup. The other authors declare no conflicts of interest. (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/).)
Databáze:	MEDLINE
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