ArticleOncology letters2024
Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis.
Article in Oncology letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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.
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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, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Computed Tomography-Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of early recurrent of intrahepatic cholangiocarcinoma (ICC) has been widely investigated; however, the predictive value is currently insufficient. To determine the effectiveness of machine learning (ML) for the diagnosis of early recurrent intrahepatic cholangiocarcinoma (ICC), particularly in comparison with clinical models, the present study aimed to determine which ML model had the best diagnostic performance for inpatients with recurrent ICC. In order to search for studies which could be included, three electronic databases were screened from inception to November 2023. A pairwise meta-analysis was performed to evaluate the diagnostic accuracy of the random effects model. A network meta-analysis was performed to identify the most effective ML-based diagnostic model based on the surface under the cumulative ranking curve score. A total of 5 studies of acceptable quality containing 1,247 patients with ICC were included in the present study. Following pairwise meta-analysis, it was found that the ML-based diagnostic accuracy was greater than that of clinical models (surface under the cumulative ranking curve score closer to 1, with significant differences), which initially proved that the ML-based diagnostic power was more optimal than that of clinical models. According to the network meta-analysis, the nomogram performed the best, indicating that this ML model achieved the best diagnostic accuracy for patients with recurrent ICC. In conclusion, the application of ML-based diagnostic models for patients with recurrent ICC was more optimal than the application of the clinical model. The nomogram model ranked first among the models and is therefore recommended for patients with recurrent ICC.
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