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Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis

Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis

In contrast, other studies, such as Li et al [19] and Wang et al [20], observed considerably lower performance, with AUC values of 0.72 and 0.73, respectively. These discrepancies can be attributed to factors such as data quality, sample size, and model architecture. Low-quality datasets, such as retrospective studies or single-center studies, may introduce selection bias and limit the generalizability of models, thereby affecting the reliability of radiomics approaches in clinical practice [21].

Lizhen She, Yunfeng Li, Hongyong Wang, Jun Zhang, Yuechen Zhao, Jie Cui, Ling Qiu

J Med Internet Res 2025;27:e71091