Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis
Abstract Background Complete Cytoreduction (CC) in ovarian cancer (OC) has been associated with better outcomes. Outcomes after CC have a multifactorial and interrelated cause that may not be predictable by conventional statistical methods. Artificial intelligence (AI) may be more accurate in predic...
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BMC
2025-01-01
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| Online Access: | https://doi.org/10.1186/s12893-025-02766-3 |
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| author | Somayyeh Noei Teymoordash Hoda Zendehdel Ali Reza Norouzi Mahdis Kashian |
| author_facet | Somayyeh Noei Teymoordash Hoda Zendehdel Ali Reza Norouzi Mahdis Kashian |
| author_sort | Somayyeh Noei Teymoordash |
| collection | DOAJ |
| description | Abstract Background Complete Cytoreduction (CC) in ovarian cancer (OC) has been associated with better outcomes. Outcomes after CC have a multifactorial and interrelated cause that may not be predictable by conventional statistical methods. Artificial intelligence (AI) may be more accurate in predicting outcomes. This systematic review aimed to determine the accuracy of AI compared to traditional statistics in predicting outcomes after CC in OC. Methods PubMed, Scopus, Google Scholar, Embase, and Web of Science databases were searched with Mesh terms to find studies that investigated the role of AI in predicting outcomes after CC in EOC from the beginning of 2015 to February 2024. The outcomes included overall survival (OS), removal of all macroscopic disease (R0), length of hospital stay (LOS), and intensive care unit (ICU) admission. This systematic review was conducted based on the PRISMA guidelines. Heterogeneity between studies was evaluated using the I2 test. Egger’s test was used to check publication bias. Results Ten studies (3460 participants) were included. The pooled estimate of 3 studies showed that the accuracy of AI for predicting OS was (Mean: 69.64%, CI 95%:66.50, 72.78%, I2:0%). The pooled estimate of 4 studies showed that the accuracy of AI for predicting R0 was (Mean: 80.5%, CI 95%:71.46, 89.6%, I2:47.9%). The use of AI in predicting outcomes, including ICU admission, urinary tract infection (UTI), and LOS was investigated in one study, and the AUC of AI for predicting all three outcomes was approximately 90%. Conclusion AI may accurately predict the outcomes after CC in OC patients. Most studies agree that Artificial Neural Networks (ANN) and Machine Learning (ML) models outperform conventional statistics in predicting postoperative outcomes. |
| format | Article |
| id | doaj-art-e27e30da436646a7a07d7ec8a731f3e2 |
| institution | Kabale University |
| issn | 1471-2482 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Surgery |
| spelling | doaj-art-e27e30da436646a7a07d7ec8a731f3e22025-01-19T12:07:57ZengBMCBMC Surgery1471-24822025-01-0125111110.1186/s12893-025-02766-3Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysisSomayyeh Noei Teymoordash0Hoda Zendehdel1Ali Reza Norouzi2Mahdis Kashian3Department of Obstetrics and Gynecology, Firoozgar Clinical Research and Development Center (FCRDC), School of Medicine, Iran University of Medical SciencesDepartment of Obstetrics and Gynecology, Firoozgar Clinical Research and Development Center (FCRDC), School of Medicine, Iran University of Medical SciencesPediatric Respiratory Diseases Research Center (PRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical SciencesDepartment of Obstetrics and Gynecology, Firoozgar Clinical Research and Development Center (FCRDC), School of Medicine, Iran University of Medical SciencesAbstract Background Complete Cytoreduction (CC) in ovarian cancer (OC) has been associated with better outcomes. Outcomes after CC have a multifactorial and interrelated cause that may not be predictable by conventional statistical methods. Artificial intelligence (AI) may be more accurate in predicting outcomes. This systematic review aimed to determine the accuracy of AI compared to traditional statistics in predicting outcomes after CC in OC. Methods PubMed, Scopus, Google Scholar, Embase, and Web of Science databases were searched with Mesh terms to find studies that investigated the role of AI in predicting outcomes after CC in EOC from the beginning of 2015 to February 2024. The outcomes included overall survival (OS), removal of all macroscopic disease (R0), length of hospital stay (LOS), and intensive care unit (ICU) admission. This systematic review was conducted based on the PRISMA guidelines. Heterogeneity between studies was evaluated using the I2 test. Egger’s test was used to check publication bias. Results Ten studies (3460 participants) were included. The pooled estimate of 3 studies showed that the accuracy of AI for predicting OS was (Mean: 69.64%, CI 95%:66.50, 72.78%, I2:0%). The pooled estimate of 4 studies showed that the accuracy of AI for predicting R0 was (Mean: 80.5%, CI 95%:71.46, 89.6%, I2:47.9%). The use of AI in predicting outcomes, including ICU admission, urinary tract infection (UTI), and LOS was investigated in one study, and the AUC of AI for predicting all three outcomes was approximately 90%. Conclusion AI may accurately predict the outcomes after CC in OC patients. Most studies agree that Artificial Neural Networks (ANN) and Machine Learning (ML) models outperform conventional statistics in predicting postoperative outcomes.https://doi.org/10.1186/s12893-025-02766-3Artificial intelligenceComplete surgical cytoreductionOvarian CancerOutcomesPrediction |
| spellingShingle | Somayyeh Noei Teymoordash Hoda Zendehdel Ali Reza Norouzi Mahdis Kashian Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis BMC Surgery Artificial intelligence Complete surgical cytoreduction Ovarian Cancer Outcomes Prediction |
| title | Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis |
| title_full | Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis |
| title_fullStr | Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis |
| title_full_unstemmed | Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis |
| title_short | Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer: a systematic review and meta-analysis |
| title_sort | diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian cancer a systematic review and meta analysis |
| topic | Artificial intelligence Complete surgical cytoreduction Ovarian Cancer Outcomes Prediction |
| url | https://doi.org/10.1186/s12893-025-02766-3 |
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