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Review
. 2023 Jan-Feb;12(1):50-58.
doi: 10.4103/EUS-D-21-00131.

The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review

Hua Yin  1 , Xiaoli Yang  1 , Liqi Sun  2 , Peng Pan  2 , Lisi Peng  2 , Keliang Li  3 , Deyu Zhang  2 , Fang Cui  2 , Chuanchao Xia  2 , Haojie Huang  2 , Zhaoshen Li  2
Affiliations
Review

The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review

Hua Yin et al. Endosc Ultrasound. 2023 Jan-Feb.

Abstract

Conventional EUS plays an important role in identifying pancreatic cancer. However, the accuracy of EUS is strongly influenced by the operator's experience in performing EUS. Artificial intelligence (AI) is increasingly being used in various clinical diagnoses, especially in terms of image classification. This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of pancreatic cancer using EUS images. We searched the Embase, PubMed, and Cochrane Library databases to identify studies that used endoscopic ultrasound images of pancreatic cancer and AI to predict the diagnostic accuracy of pancreatic cancer. Two reviewers extracted the data independently. The risk of bias of eligible studies was assessed using a Deek funnel plot. The quality of the included studies was measured by the QUDAS-2 tool. Seven studies involving 1110 participants were included: 634 participants with pancreatic cancer and 476 participants with nonpancreatic cancer. The accuracy of the AI for the prediction of pancreatic cancer (area under the curve) was 0.95 (95% confidence interval [CI], 0.93-0.97), with a corresponding pooled sensitivity of 93% (95% CI, 0.90-0.95), specificity of 90% (95% CI, 0.8-0.95), positive likelihood ratio 9.1 (95% CI 4.4-18.6), negative likelihood ratio 0.08 (95% CI 0.06-0.11), and diagnostic odds ratio 114 (95% CI 56-236). The methodological quality in each study was found to be the source of heterogeneity in the meta-regression combined model, which was statistically significant (P = 0.01). There was no evidence of publication bias. The accuracy of AI in diagnosing pancreatic cancer appears to be reliable. Further research and investment in AI could lead to substantial improvements in screening and early diagnosis.

Keywords: AI; EUS; accuracy; artificial intelligence; pancreatic cancer; predicting pancreatic ductal adenocarcinoma.

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Conflict of interest statement

None

Figures

Figure 1
Figure 1
Flow diagram of identification of relevant research
Figure 2
Figure 2
Quality assessment of diagnostic accuracy studies – 2 for the assessment of the methodological qualities of all the enrolled studies. (+) denotes low risk of bias, (?) denotes unclear risk of bias, (-) denotes high risk of bias
Figure 3
Figure 3
Deek funnel plot for studies
Figure 4
Figure 4
Summary receiver operating characteristic curve with 95% confidence region and prediction region for the prediction of pancreatic cancer in EUS images
Figure 5
Figure 5
Hierarchical summary receiver operating characteristic curve
Figure 6
Figure 6
Meta-regression for the reason of heterogeneity in the diagnostic test accuracy meta-analysis
Figure 7
Figure 7
Forest plots of sensitivity and specificity of AI for the prediction of pancreatic cancer in EUS images
Figure 8
Figure 8
Fagan normogram for the prediction of pancreatic cancer in EUS images
See this image and copyright information in PMC

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