JMIR Publications

ABSTRACT

Background:

Despite AI models showing high predictive accuracy for early CCA recurrence, their clinical use faces challenges like reproducibility, generalizability, hidden biases, and uncertain performance across diverse datasets and populations, raising concerns about practical applicability.

Objective:

This meta-analysis seeks to systematically assess the diagnostic performance of artificial intelligence (AI) models utilizing computed tomography (CT) imaging for predicting the early recurrence of cholangiocarcinoma (CCA).

Methods:

A systematic search was conducted in PubMed, Embase, and Web of Science were performed for studies published up to April 2025, focusing on the ability of CT-based AI to predict early recurrence of CCA. Heterogeneity was evaluated using the I² statistic, and data were pooled using a bivariate random-effects model. Methodological quality was assessed with an optimized version of the revised QUADAS-2 tool.

Results:

Nine studies with 30 datasets involving 1,537 patients were included. In internal validation cohorts, CT-based AI models showed a pooled sensitivity of 0.87 (95% CI: 0.81–0.92), specificity of 0.85 (95% CI: 0.79–0.89), diagnostic odds ratio (DOR) of 37.71 (95% CI: 18.35–77.51), and area under the curve (AUC) of 0.93 (95% CI: 0.90–0.94). In external validation cohorts, the pooled sensitivity was 0.87 (95% CI: 0.81–0.91), specificity was 0.82 (95% CI: 0.77–0.86), DOR was 30.81 (95% CI: 18.79–50.52), and AUC was 0.85 (95% CI: 0.82–0.88). The AUC was significantly lower in external validation than in internal validation (P < 0.001).

Conclusions:

Our results show that CT-based AI models predict early CCA recurrence with high performance in internal validation sets and moderate performance in external validation sets. Future research should focus on prospective designs and establishing standardized gold standards to further validate the clinical applicability and generalization value of AI models.