ReviewFrontiers in artificial intelligence2021
Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning.
Review in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- 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
- Reducing Educational Bias in Cognitive Assessment via Dynamic Support Vector Machine Weighting: Validation Study on an Education-Stratified Dataset.JMIR rehabilitation and assistive technologies · 2026Article
- Integrating domain-specific convolutional features with Swin Transformers for transparent cervical cancer diagnostics.Frontiers in oncology · 2026Article
- Perceived community cervical cancer prevention support and cervical cancer screening utilization among women in Shenzhen, China: a cross-sectional study.Frontiers in public health · 2026Article
- Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.Scientific reports · 2025Article
- Evaluating the Cost-Effectiveness of Cervical Cancer Screening and Treatment in Western Romania.Current oncology (Toronto, Ont.) · 2025Article
- Identification of CDK2 as a key apoptotic gene for predicting cervical cancer prognosis using bioinformatics and machine learning.American journal of cancer research · 2025Article
- Highlighting the role of CD44 in cervical cancer progression: immunotherapy's potential in inhibiting metastasis and chemoresistance.Immunologic research · 2024Review
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- (Nano)biotechnological approaches in the treatment of cervical cancer: integration of engineering and biology.Frontiers in immunology · 2024Review
- Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study.Frontiers in oncology · 2024Article
- Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.Diagnostics (Basel, Switzerland) · 2023Review
- Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- Imaging Biomarkers and Liquid Biopsy in Assessment of Cervical Cancer.Journal of computer assisted tomographyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use.
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What OpenQuestion holds
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.