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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 2024, Zhang et al [22] conducted a meta-analysis specifically examining MRI-based radiomics models for predicting LVSI in cervical cancer. Their findings reported a sensitivity of 0.79, specificity of 0.73, and an AUC of 0.83. In contrast, our study achieved superior results in the internal validation set, with a sensitivity of 0.84, specificity of 0.79, and an AUC of 0.88.

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

J Med Internet Res 2025;27:e71091

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Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

For instance, a study by Zhang et al [5] on Chinese university students found that individuals classified as high-risk for SNA exhibited significantly higher levels of anxiety and depressive symptoms compared to low-risk groups. In addition, SNA is closely associated with decreased sleep quality, impaired academic performance, and social dysfunction [6,7].

Lin Luo, Junfeng Yuan, Chen Xu, Huilin Xu, Haojie Tan, Yinhao Shi, Haiping Zhang, Haijun Xi

J Med Internet Res 2025;27:e72260

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