ReviewFrontiers in oncology2022
Machine Learning for Endometrial Cancer Prediction and Prognostication.
Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 59 citations in OpenAlex.
- Hematological Versus Clinical Predictors of Tumor Grade in Endometrial Cancer: A Comparative Machine Learning Study with SHAP-Based Explainability.Diagnostics (Basel, Switzerland) · 2026Article
- Molecular and Immune Profiling of Sessile Serrated Adenomas/Polyps Reveals Prognostic Genes and Immune Cell Dynamics in Colorectal Cancer Progression.Applied biochemistry and biotechnology · 2026Article
- Atranorin suppresses the LUCAT1/STAT3 axis to induce ferroptotic cell death in ovarian cancer.Molecular biology reports · 2026Article
- Small nucleolar RNA host genes in hepatocellular carcinoma: an evidence-weighted and etiology-aware framework for mechanistic and translational interpretation.Molecular biology reports · 2026Review
- Emerging molecular targets in gynaecologic cancers: clinical implications for personalized therapy.Molecular biology reports · 2026Review
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Metabolic plasticity in pancreatic ductal adenocarcinoma progression and response to treatment.Molecular cancer · 2026Review
- Inflammatory milieu and role of epigenetic modifications in high-grade serous ovarian cancer.Clinical epigenetics · 2026Review
- Frequency Ranking of Imaging Biomarkers for Lung Cancer Risk Stratification Using a Hybrid Elastic Net Method.Cancers · 2026Article
- Review
- MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.Frontiers in oncology · 2026Article
- Predictive value of thyroid autoantibodies for coronary heart disease severity in individuals with normal thyroid function based on machine learning and SHAP interpretation.Frontiers in immunology · 2026Article
- CITOBOT AI for real-world cervical cancer screening using colposcopy imaging.Frontiers in public health · 2026Article
- Predictive biomarkers for immunotherapy response in endometrial cancer: current insights and future directions.Exploration of targeted anti-tumor therapy · 2026Review
- Evaluation of drug options for epithelial ovarian cancer (EOC) treatment using the analytical hierarchy process improves decision-making transparency.Discover oncology · 2025Article
- Long non-coding RNAs and VEGF in ovarian cancer: mechanisms and therapeutic potential.Journal of ovarian research · 2025Review
- RNA sequencing in ovarian cancer research: a comprehensive review.Journal of ovarian research · 2025Review
- Molecular Prognosticators Guiding Fertility-Sparing Surgery in Early-Stage Endometrial Cancer: A Comprehensive Review.Cancers · 2025Review
- Biological characteristics prediction of endometrial cancer based on deep convolutional neural network and multiparametric MRI radiomics.Abdominal radiology (New York) · 2025Article
- Signaling networks and MiRNA crosstalk in ovarian cancer chemoresistance.Journal of ovarian research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
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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.