Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis
BackgroundBreast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1420328/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850150813710680064 |
|---|---|
| author | Zohreh Javanmard Saba Zarean Shahraki Kosar Safari Abbas Omidi Sadaf Raoufi Mahsa Rajabi Mohammad Esmaeil Akbari Mehrad Aria |
| author_facet | Zohreh Javanmard Saba Zarean Shahraki Kosar Safari Abbas Omidi Sadaf Raoufi Mahsa Rajabi Mohammad Esmaeil Akbari Mehrad Aria |
| author_sort | Zohreh Javanmard |
| collection | DOAJ |
| description | BackgroundBreast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study’s systematic review and meta-analysis.MethodsThree online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms (“Breast Cancer”, “Survival Prediction”, and “Machine Learning”) and their synonyms. Original articles applying ML algorithms for BC survival prediction using clinical data were included. The quality of studies was assessed via the Qiao Quality Assessment tool.ResultsAmongst 140 identified articles, 32 met the eligibility criteria. Analyzed ML methods achieved a mean validation accuracy of 89.73%. Hybrid models, combining traditional and modern ML techniques, were mostly considered to predict survival rates (40.62%). Supervised learning was the dominant ML paradigm (75%). Common ML methodologies included pre-processing, feature extraction, dimensionality reduction, and classification. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), emerged as the preferred modern algorithm within these methodologies. Notably, 81.25% of studies relied on internal validation, primarily using K-fold cross-validation and train/test split strategies.ConclusionThe findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. However, to ensure the robustness and generalizability of these predictive models, future research should emphasize the importance of rigorous external validation. Such endeavors will not only validate the efficacy of these models across diverse populations but also pave the way for their integration into clinical practice, ultimately contributing to personalized patient care and improved survival outcomes.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024513350. |
| format | Article |
| id | doaj-art-2ba8cba738f642eda449d742bcaa43da |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-2ba8cba738f642eda449d742bcaa43da2025-08-20T02:26:27ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.14203281420328Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysisZohreh Javanmard0Saba Zarean Shahraki1Kosar Safari2Abbas Omidi3Sadaf Raoufi4Mahsa Rajabi5Mohammad Esmaeil Akbari6Mehrad Aria7Department of Health Information Management, School of Allied Medical Sciences, Tehran 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 Aerospace Engineering, Khaje Nasir Toosi University of Technology, Tehran, IranDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB, CanadaDepartment of Computer Science, University of Arizona, Tucson, AZ, United StatesDepartment of Electrical Engineering, University of Guilan, Rasht, IranCancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IranCancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IranBackgroundBreast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study’s systematic review and meta-analysis.MethodsThree online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms (“Breast Cancer”, “Survival Prediction”, and “Machine Learning”) and their synonyms. Original articles applying ML algorithms for BC survival prediction using clinical data were included. The quality of studies was assessed via the Qiao Quality Assessment tool.ResultsAmongst 140 identified articles, 32 met the eligibility criteria. Analyzed ML methods achieved a mean validation accuracy of 89.73%. Hybrid models, combining traditional and modern ML techniques, were mostly considered to predict survival rates (40.62%). Supervised learning was the dominant ML paradigm (75%). Common ML methodologies included pre-processing, feature extraction, dimensionality reduction, and classification. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), emerged as the preferred modern algorithm within these methodologies. Notably, 81.25% of studies relied on internal validation, primarily using K-fold cross-validation and train/test split strategies.ConclusionThe findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. However, to ensure the robustness and generalizability of these predictive models, future research should emphasize the importance of rigorous external validation. Such endeavors will not only validate the efficacy of these models across diverse populations but also pave the way for their integration into clinical practice, ultimately contributing to personalized patient care and improved survival outcomes.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024513350.https://www.frontiersin.org/articles/10.3389/fonc.2024.1420328/fullbreast cancersurvival predictionmachine learningdeep learningclinical datasystematic review |
| spellingShingle | Zohreh Javanmard Saba Zarean Shahraki Kosar Safari Abbas Omidi Sadaf Raoufi Mahsa Rajabi Mohammad Esmaeil Akbari Mehrad Aria Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis Frontiers in Oncology breast cancer survival prediction machine learning deep learning clinical data systematic review |
| title | Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis |
| title_full | Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis |
| title_fullStr | Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis |
| title_full_unstemmed | Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis |
| title_short | Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis |
| title_sort | artificial intelligence in breast cancer survival prediction a comprehensive systematic review and meta analysis |
| topic | breast cancer survival prediction machine learning deep learning clinical data systematic review |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1420328/full |
| work_keys_str_mv | AT zohrehjavanmard artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT sabazareanshahraki artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT kosarsafari artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT abbasomidi artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT sadafraoufi artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT mahsarajabi artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT mohammadesmaeilakbari artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis AT mehradaria artificialintelligenceinbreastcancersurvivalpredictionacomprehensivesystematicreviewandmetaanalysis |