Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis.
Authors
Affiliations (8)
Affiliations (8)
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
- Musculoskeletal Imaging Research Center (MIRC), Tehran University of Medical Sciences, Tehran, Iran.
- Medical Students Research Committee, Shahed University, Tehran, Iran.
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
- Department of Radiology, University of Louisville School of Medicine, 530 S Jackson St. Suite C07, Louisville, KY, 40202, USA.
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
- Department of Radiology, University of Louisville School of Medicine, 530 S Jackson St. Suite C07, Louisville, KY, 40202, USA. [email protected].
Abstract
We conducted a systematic review and meta-analysis in diagnostic performance of studies that tried to use artificial intelligence (AI) algorithms in detecting pancreatic ductal adenocarcinoma (PDAC) and distinguishing them from other types of pancreatic lesions. We systematically searched for studies on pancreatic lesions and AI from January 2014 to May 2024. Data were extracted and a meta-analysis was performed using contingency tables and a random-effects model to calculate pooled sensitivity and specificity. Quality assessment was done using modified TRIPOD and PROBAST tools. We included 26 studies in this systematic review, with 22 studies chosen for meta-analysis. The evaluation of AI algorithms' performance in internal validation exhibited a pooled sensitivity of 93% (95% confidence interval [CI], 90 to 95) and specificity of 95% (95% CI, 92 to 97). Additionally, externally validated AI algorithms demonstrated a combined sensitivity of 89% (95% CI, 85 to 92) and specificity of 91% (95% CI, 85 to 95). Subgroup analysis indicated that diagnostic performance differed by comparator group, image contrast, segmentation technique, and algorithm type, with contrast-enhanced imaging and specific AI models (e.g., random forest for sensitivity and CNN for specificity) demonstrating superior accuracy. Although the potential biases should be further addressed, results of this systematic review and meta-analysis showed that AI models have the potential to be incorporated in clinical settings for the detection of smaller tumors and underpinning early signs of PDAC.