{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T03:03:17Z","timestamp":1790996597419,"version":"4.1.0"},"reference-count":70,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Reports"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. Key prognostic endpoints, including overall survival (OS), recurrence\u2010free survival (RFS), progression\u2010free survival (PFS), and treatment response prediction (TRP), are examined to evaluate the effectiveness of these algorithms and identify significant features that influence predictive accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Recent Findings<\/jats:title><jats:p>A thorough search of four major databases\u2014PubMed, Scopus, Web of Science, and Cochrane\u2014resulted in 2400 articles published within the last decade, with 32 studies meeting the inclusion criteria. Notably, most publications emerged after 2021. Commonly used algorithms for survival prediction included random forest, support vector machines, logistic regression, XGBoost, and various deep learning models. Evaluation metrics such as area under the curve (AUC) (18 studies), concordance index (C\u2010index) (11 studies), and accuracy (11 studies) were frequently employed. Age at diagnosis, tumor stage, CA\u2010125 levels, and treatment\u2010related factors were consistently highlighted as significant predictors, emphasizing their relevance in OC prognosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ML models demonstrate considerable potential for predicting OC survival outcomes; however, challenges persist regarding model accuracy and interpretability. Incorporating diverse data types\u2014such as clinical, imaging, and molecular datasets\u2014holds promise for enhancing predictive capabilities. Future advancements will depend on integrating heterogeneous data sources with multimodal ML approaches, which are crucial for improving prognostic precision in OC.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cnr2.70138","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:47:11Z","timestamp":1742374031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0939-7983","authenticated-orcid":false,"given":"Farkhondeh","family":"Asadi","sequence":"first","affiliation":[{"name":"Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2750-3826","authenticated-orcid":false,"given":"Milad","family":"Rahimi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4669-7483","authenticated-orcid":false,"given":"Nahid","family":"Ramezanghorbani","sequence":"additional","affiliation":[{"name":"Department of Development &amp; Coordination Scientific Information and Publications Deputy of Research &amp; Technology, Ministry of Health &amp; Medical Education  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0707-8399","authenticated-orcid":false,"given":"Sohrab","family":"Almasi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-024-01219-x"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.2174\/1568026620999201111155426"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)62146-7"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32552-2"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.2147\/IJWH.S197604"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molonc.2009.02.001"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.07.001"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djy071"},{"issue":"4","key":"e_1_2_13_11_1","first-page":"328","article-title":"How to Use an Article About Prognosis","volume":"52","author":"Hansebout R. 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