Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.
Purpose: This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC). Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta...
| Publicado en: | Abdominal Radiology Vol. 51; no. 2; pp. 977 - 991 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191808260&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191808260 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2026 vid: 51 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191808260 186331555 10.1007/s00261-025-05089-2 191808260 ppf: 977 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. aug: au: Yan, Guangzhao Chen, Xuming Wang, Yanyan affil: Department of Emergency Medicine, Emergency and Critical Care Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China sug: ab: Purpose: This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC). Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) guidelines, a comprehensive literature search was conducted across PubMed, Embase, and Web of Science from inception to March 2025. Bivariate random-effects models pooled sensitivity, specificity, and area under the curve (AUC). Heterogeneity was quantified via I² statistics, with subgroup analyses examining sources of variability, including AI methodologies, model architectures, sample sizes, geographic distributions, control groups and tumor stages. Results: Nineteen studies involving 5,986 patients in internal validation cohorts and 2,069 patients in external validation cohorts were included. AI models demonstrated robust diagnostic accuracy in internal validation, with pooled sensitivity of 0.94 (95% CI 0.89–0.96), specificity of 0.93 (95% CI 0.90–0.96), and AUC of 0.98 (95% CI 0.96–0.99). External validation revealed moderately reduced sensitivity (0.84; 95% CI 0.78–0.89) and AUC (0.94; 95% CI 0.92–0.96), while specificity remained comparable (0.93; 95% CI 0.87–0.96). Substantial heterogeneity (I² > 85%) was observed, predominantly attributed to methodological variations in AI architectures and disparities in cohort sizes. Conclusions: AI demonstrates excellent diagnostic performance for PDAC on CECT, achieving high sensitivity and specificity across validation scenarios. However, its efficacy varies significantly with clinical context and tumor stage. Therefore, prospective multicenter trials that utilize standardized protocols and diverse cohorts, including early-stage tumors and complex benign conditions, are essential to validate the clinical utility of AI. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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