ArticleInternational journal of molecular sciences2019
A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Molecular and AI enabled prognostication in endometrial cancer a 2015 to 2024 bibliometric Atlas and critical review.Discover oncology · 2026Article
- A clinically translatable pathomics-based predictive model for preoperative prognostic assessment in patients with endometrial cancer.Discover oncology · 2026Article
- Particles and Prejudice: Nanomedicine Approaches to Reducing Health Disparities in Endometrial Cancer.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation.Cancers · 2023Article
- FAT2 mutation is associated with better prognosis and responsiveness to immunotherapy in uterine corpus endometrial carcinoma.Cancer medicine · 2023Article
- Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.International journal of molecular sciences · 2022Article
- Construction and validation of a prognostic model based on 11 lymph node metastasis-related genes for overall survival in endometrial cancer.Cancer medicine · 2022Article
- Can miRNAs be useful biomarkers in improving prognostic stratification in endometrial cancer patients? An update review.International journal of cancer · 2022Review
- Review
- Creation and validation of models to predict response to primary treatment in serous ovarian cancer.Scientific reports · 2021Article
- Article
- Differential DNA methylation in high-grade serous ovarian cancer (HGSOC) is associated with tumor behavior.Scientific reports · 2019Article
- Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA.International journal of molecular sciences · 2019Article
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Authors and funding
13 authors.
Funding
Abstract
The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.
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