Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy DOI Creative Commons
Mauro Francesco Pio Maiorano, Gennaro Cormio, Vera Loizzi

et al.

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 84 - 84

Published: April 18, 2025

Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review meta-analysis was conducted to assess the performance AI-driven models across three key domains: genomics molecular profiling, radiomics-based imaging analysis, immunotherapy response. Methods: Relevant studies were identified a search multiple databases (2020–2025), adhering PRISMA guidelines. Results: Thirteen met inclusion criteria, involving over 10,000 patients encompassing diverse AI such as machine learning classifiers deep architectures. Pooled AUCs indicated strong predictive for genomics-based (0.78), (0.88), immunotherapy-based (0.77) models. Notably, radiogenomics-based integrating data yielded highest accuracy (AUC = 0.975), highlighting potential multi-modal approaches. Heterogeneity risk bias assessed, evidence certainty graded. Conclusions: Overall, demonstrated promise predicting therapeutic outcomes cancer, with radiomics integrated radiogenomics emerging leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, integration immune spatial transcriptomic support clinical implementation individualized treatment Unlike earlier reviews, this study synthesizes broader range applications provides pooled metrics It examines methodological soundness selected highlights current gaps opportunities translation, offering comprehensive forward-looking perspective field.

Language: Английский

Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy DOI Creative Commons
Mauro Francesco Pio Maiorano, Gennaro Cormio, Vera Loizzi

et al.

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 84 - 84

Published: April 18, 2025

Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review meta-analysis was conducted to assess the performance AI-driven models across three key domains: genomics molecular profiling, radiomics-based imaging analysis, immunotherapy response. Methods: Relevant studies were identified a search multiple databases (2020–2025), adhering PRISMA guidelines. Results: Thirteen met inclusion criteria, involving over 10,000 patients encompassing diverse AI such as machine learning classifiers deep architectures. Pooled AUCs indicated strong predictive for genomics-based (0.78), (0.88), immunotherapy-based (0.77) models. Notably, radiogenomics-based integrating data yielded highest accuracy (AUC = 0.975), highlighting potential multi-modal approaches. Heterogeneity risk bias assessed, evidence certainty graded. Conclusions: Overall, demonstrated promise predicting therapeutic outcomes cancer, with radiomics integrated radiogenomics emerging leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, integration immune spatial transcriptomic support clinical implementation individualized treatment Unlike earlier reviews, this study synthesizes broader range applications provides pooled metrics It examines methodological soundness selected highlights current gaps opportunities translation, offering comprehensive forward-looking perspective field.

Language: Английский

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