ArticleBMC bioinformatics2021
Machine learning-based prediction of survival prognosis in cervical cancer.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Cervical cancer survival prediction by machine learning algorithms: a systematic review.BMC cancer · 2023Pooled it
- Unsupervised clustering analysis unravels the role of systemic inflammatory indices in the prognosis of patients with locally advanced cervical cancer treated with chemoradiation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Machine learning-based prediction of ICU admission and mortality in Crimean-Congo hemorrhagic fever by using wide-range targeted metabolomics.BMC infectious diseases · 2026Article
- Prognostic utility of systemic immune-inflammation markers in locally advanced cervical cancer undergoing radical radiotherapy.The oncologist · 2026Article
- Enhanced temporal encoding-decoding for survival analysis of multimodal clinical data in smart healthcare.Visual computing for industry, biomedicine, and art · 2025Article
- Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy.Current oncology (Toronto, Ont.) · 2025Article
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.BMC medical informatics and decision making · 2025Article
- The role and diagnostic value of deregulated miRNAs in cervical cancer.Discover oncology · 2025Review
- Article
- Research on a method for early diagnosis and progression prediction of cervical cancer based on imaging omics and molecular omics.Frontiers in oncology · 2025Article
- Predicting Factors Affecting Survival Rate in Patients Undergoing On-Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review.Health science reports · 2025Review
- Development and Internal-External Validation Models for Cervical Cancer Overall Survival Prognosis: A SEER-Based Study and Chinese Data.International journal of women's health · 2025Article
- Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.Scientific reports · 2024Article
- Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer.Scientific reports · 2024Article
- Review
- Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis.Biomedical optics express · 2024Article
- A bibliometric review of predictive modelling for cervical cancer risk.Frontiers in research metrics and analytics · 2024Review
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model.
resultsThe expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells.
conclusionA miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).
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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.