This paper is in the following e-collection/theme issue:

Digital Health Reviews (1742) Precision Medicine (163) Clinical Information and Decision Making (1935) Machine Learning (2095) Formative Evaluation of Digital Health Interventions (3272)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48527, first published .
Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Authors of this article:

Qingyi Wang1 Author Orcid Image ;   Zhuo Chang2 Author Orcid Image ;   Xiaofang Liu1 Author Orcid Image ;   Yunrui Wang1 Author Orcid Image ;   Chuwen Feng1 Author Orcid Image ;   Yunlu Ping1 Author Orcid Image ;   Xiaoling Feng3 Author Orcid Image
Article Authors Cited by (11) Tweetations (5) Metrics

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  10. Jiang D, Liu Z, Wang K, Qian Y, Feng J, Gong L, Ren J, Xiang Y, Zhang F, Liu L, Zhou H, Liang C, Wei W, Zang B, Kong C, Li Y, Cheng S. Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma. BMC Gastroenterology 2025 View
  11. Eldesouki R, Tarek O, Morsi H. Integrating multi-omics and clinical features to model survival in epithelial ovarian cancer subtypes. Scientific Reports 2025;15(1) View