ArticleBritish journal of cancer2020
Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma.
Article in British journal of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Molecular Mechanisms of Lymph Node Metastasis in Gallbladder Cancer: Insights into the Tumor Microenvironment.Biomedicines · 2025Review
- Assessment of Cyclin D1 Expression: Prognostic Value and Functional Insights in Endometrial Cancer: In Silico Study.International journal of molecular sciences · 2025Article
- Using an MRI-based radiomics model to predict recurrence of endometrial cancer: a step towards meeting a key clinical need.European radiology · 2023Article
- Expression of EMT-related genes in lymph node metastasis in endometrial cancer: a TCGA-based study.World journal of surgical oncology · 2023Article
- Preoperative pelvic MRI and 2-[European radiology · 2023Article
- A 4-Gene Signature Associated With Recurrence in Low- and Intermediate-Risk Endometrial Cancer.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundIn endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.
methodsProtein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.
resultsLNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype.
conclusionsWe demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.
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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.