Table 2_Deep learning models for pancreatic cancer detection on CT: a meta-analysis.docx

Quality assessment was performed using standardized tools for risk of bias and methodological soundness. A subgroup analysis focused on scans acquired 3 to 36 months prior to clinical diagnosis. Fifteen studies were included.

The pooled results demonstrated a sensitivity of 0.92 (95% confidence interval: 0.88–0.94), specificity of 0.96 (95% CI: 0.92–0.98), diagnostic odds ratio of 285.00 (95% CI, 97.00–839.00), and area under the curve of 0.97 (95% CI, 0.95–0.98). In the preclinical diagnosis subgroup, the pooled sensitivity was 0.73 and specificity was 0.92. This meta-analysis validates the high diagnostic accuracy of deep learning models for detecting pancreatic cancer on CT scans in retrospective datasets.

However, all included studies were retrospective, external validation was inconsistent, and no prospective or real-world deployment evidence exists to date. Therefore, while these findings suggest the potential of DL as a decision-support tool, they should be interpreted with caution, and prospective multicenter validation studies are urgently needed before clinical integration can be recommended. However, the pooled estimates should be interpreted cautiously because only one representative model per study was included.</p>