SynthesisBMC medical imaging2026
Diagnostic performance of artificial intelligence versus conventional imaging for differentiating G2/G3 from G1 pancreatic neuroendocrine tumors: a systematic review and meta-analysis.
Synthesis in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Pancreatic Endoscopic Ultrasonography: From Image-Based Diagnosis to Cytopathology.Journal of clinical medicine · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundAccurate preoperative grading of pancreatic neuroendocrine tumors (PanNETs), specifically differentiating G2/G3 from G1, is pivotal for treatment planning but challenging with conventional biopsy. This study aims to evaluate the diagnostic performance of imaging modalities, particularly comparing Machine Learning/Deep Learning (ML/DL) algorithms against conventional expert interpretation.
methodsWe conducted a systematic review and meta-analysis of studies from PubMed, Embase, Web of Science, and Cochrane Library up to December 2025. Studies utilizing CT, MRI, or endoscopic ultrasound (EUS) for PanNET grading were included. A bivariate random-effects model was used to calculate pooled sensitivity, specificity, and area under the curve (AUC). Subgroup analyses were performed to investigate heterogeneity and the impact of validation strategies.
resultsSeventeen studies comprising 928 patients were included. The overall pooled sensitivity and specificity were 0.77 (95% CI: 0.71–0.83) and 0.83 (95% CI: 0.77–0.87), respectively, with an AUC of 0.87. Notably, ML/DL models demonstrated significantly higher sensitivity than conventional imaging (0.84 vs. 0.71, p < 0.01) but lower specificity (0.78 vs. 0.87, p < 0.01). Multicenter studies showed a trend toward higher diagnostic metrics compared to single-center cohorts. Interestingly, the performance gap between external and internal validation narrowed when restricted to AI subgroups, suggesting the robustness of modern algorithms.
conclusionsImaging-based analysis offers high diagnostic accuracy for PanNET grading. A theoretical sequential diagnostic strategy is suggested: utilizing AI’s high sensitivity for initial screening, followed by expert radiological review to ensure specificity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.