Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review
ABSTRACT Background 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‐free survival (RFS), progression‐free survival (PFS), and treatm...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Cancer Reports |
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| Online Access: | https://doi.org/10.1002/cnr2.70138 |
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| author | Farkhondeh Asadi Milad Rahimi Nahid Ramezanghorbani Sohrab Almasi |
| author_facet | Farkhondeh Asadi Milad Rahimi Nahid Ramezanghorbani Sohrab Almasi |
| author_sort | Farkhondeh Asadi |
| collection | DOAJ |
| description | ABSTRACT Background 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‐free survival (RFS), progression‐free survival (PFS), and treatment response prediction (TRP), are examined to evaluate the effectiveness of these algorithms and identify significant features that influence predictive accuracy. Recent Findings A thorough search of four major databases—PubMed, Scopus, Web of Science, and Cochrane—resulted 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‐index) (11 studies), and accuracy (11 studies) were frequently employed. Age at diagnosis, tumor stage, CA‐125 levels, and treatment‐related factors were consistently highlighted as significant predictors, emphasizing their relevance in OC prognosis. Conclusion ML models demonstrate considerable potential for predicting OC survival outcomes; however, challenges persist regarding model accuracy and interpretability. Incorporating diverse data types—such as clinical, imaging, and molecular datasets—holds 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. |
| format | Article |
| id | doaj-art-eb640f097d64478aa4f3b4a328b465a3 |
| institution | OA Journals |
| issn | 2573-8348 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Reports |
| spelling | doaj-art-eb640f097d64478aa4f3b4a328b465a32025-08-20T02:10:28ZengWileyCancer Reports2573-83482025-03-0183n/an/a10.1002/cnr2.70138Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic ReviewFarkhondeh Asadi0Milad Rahimi1Nahid Ramezanghorbani2Sohrab Almasi3Department of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran IranDepartment of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran IranDepartment of Development & Coordination Scientific Information and Publications Deputy of Research & Technology, Ministry of Health & Medical Education Tehran IranDepartment of Health Information Technology and Management, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran IranABSTRACT Background 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‐free survival (RFS), progression‐free survival (PFS), and treatment response prediction (TRP), are examined to evaluate the effectiveness of these algorithms and identify significant features that influence predictive accuracy. Recent Findings A thorough search of four major databases—PubMed, Scopus, Web of Science, and Cochrane—resulted 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‐index) (11 studies), and accuracy (11 studies) were frequently employed. Age at diagnosis, tumor stage, CA‐125 levels, and treatment‐related factors were consistently highlighted as significant predictors, emphasizing their relevance in OC prognosis. Conclusion ML models demonstrate considerable potential for predicting OC survival outcomes; however, challenges persist regarding model accuracy and interpretability. Incorporating diverse data types—such as clinical, imaging, and molecular datasets—holds 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.https://doi.org/10.1002/cnr2.70138artificial intelligencecancermachine learningovarysurvival |
| spellingShingle | Farkhondeh Asadi Milad Rahimi Nahid Ramezanghorbani Sohrab Almasi Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review Cancer Reports artificial intelligence cancer machine learning ovary survival |
| title | Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review |
| title_full | Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review |
| title_fullStr | Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review |
| title_full_unstemmed | Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review |
| title_short | Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review |
| title_sort | comparing the effectiveness of artificial intelligence models in predicting ovarian cancer survival a systematic review |
| topic | artificial intelligence cancer machine learning ovary survival |
| url | https://doi.org/10.1002/cnr2.70138 |
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