Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis

Yang,Chao; Xu,Jianhui; Wang,Shuai; Wang,Ying; Zhang,Yingshi; Piao,Chengzhe

doi:10.3892/ol.2024.14518

Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis

Authors:
- Chao Yang
- Jianhui Xu
- Shuai Wang
- Ying Wang
- Yingshi Zhang
- Chengzhe Piao
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Affiliations: Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China, Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P.R. China
Published online on: June 20, 2024 https://doi.org/10.3892/ol.2024.14518
Article Number: 385

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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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