SynthesisBMC surgery2025
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.
Synthesis in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- Integration of serological markers with O-RADS ultrasound risk stratification achieves high diagnostic accuracy for early ovarian cancer: development and validation of an interpretable model.American journal of cancer research · 2026Article
- Prediction Models for Acute and Chronic Postoperative Pain in Patients with Cancer: A Systematic Review and Meta-Analysis.Journal of pain research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComplete 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.
methodsPubMed, 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 I
resultsTen 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%, I
conclusionAI 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.