Meta-Analysis
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Endosc. Aug 16, 2023; 15(8): 528-539
Published online Aug 16, 2023. doi: 10.4253/wjge.v15.i8.528
Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis
Rômulo Sérgio Araújo Gomes, Guilherme Henrique Peixoto de Oliveira, Diogo Turiani Hourneaux de Moura, Ana Paula Samy Tanaka Kotinda, Carolina Ogawa Matsubayashi, Bruno Salomão Hirsch, Matheus de Oliveira Veras, João Guilherme Ribeiro Jordão Sasso, Roberto Paolo Trasolini, Wanderley Marques Bernardo, Eduardo Guimarães Hourneaux de Moura
Rômulo Sérgio Araújo Gomes, Guilherme Henrique Peixoto de Oliveira, Diogo Turiani Hourneaux de Moura, Ana Paula Samy Tanaka Kotinda, Carolina Ogawa Matsubayashi, Bruno Salomão Hirsch, Matheus de Oliveira Veras, João Guilherme Ribeiro Jordão Sasso, Wanderley Marques Bernardo, Eduardo Guimarães Hourneaux de Moura, Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
Roberto Paolo Trasolini, Division of Hepatology and Endoscopy, Department of Gastroenterology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, United States
Author contributions: Gomes RSA contributed to the acquisition of data; Gomes RSA, de Oliveira GHP, Hirsch BS, Ribeiro Jordão Sasso JG, Matsubayashi CO, Kotinda APST, Veras MO, Moura DTH, Bernardo WM, and de Moura EGH contributed to the analysis of data; Gomes RSA, de Oliveira GHP, Hirsch BS, Ribeiro Jordão Sasso JG, Matsubayashi CO, Kotinda APST, Veras MO, Moura DTH, Bernardo WM, Trasolini RP, and de Moura EGH contributed to the interpretation of data; Gomes RSA, de Moura DTH, Trasolini RP, Bernardo WM, and de Moura EGH drafted the article; Gomes RSA, de Oliveira GHP, Hirsch BS, Ribeiro Jordão Sasso JG, Matsubayashi CO, Kotinda APST, Veras MO, de Moura DTH, Trasolini RP, Bernardo WM, and de Moura EGH revised the manuscript; Trasolini RP revised the English language; and all author approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Guilherme Henrique Peixoto de Oliveira, MD, Doctor, Medical Assistant, Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Dr Enéas de Carvalho Aguiar, 225, 6o Andar, Bloco 3, Cerqueira Cesar ZIP, São Paulo 05403-010, Brazil. dr.guilhermehpoliveira@gmail.com
Received: March 16, 2023
Peer-review started: March 16, 2023
First decision: April 20, 2023
Revised: June 15, 2023
Accepted: July 24, 2023
Article in press: July 24, 2023
Published online: August 16, 2023
Processing time: 142 Days and 22.4 Hours
Abstract
BACKGROUND

Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.

AIM

To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer.

METHODS

Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed.

RESULTS

Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists’ values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819.

CONCLUSION

AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists’ and improving their diagnostic performance in the absence of invasive procedures.

Keywords: Subepithelial lesions; Ultrasound endoscopy; Artificial intelligence

Core Tip: Artificial intelligence (AI) has shown itself as a promising tool in diagnostic endoscopic ultrasound. This systematic review and meta-analysis analyze the diagnostic performance of endoscopy ultrasound with AI for subepithelial lesions and compare it with experienced endoscopists. Based on our meta-analysis, the endoscopy ultrasound assisted for AI has high diagnostic accuracy with superiority over experienced endoscopists.