Evidence map›Paper›PMID 35939115›Full record

ArticleJournal of cancer research and clinical oncology2023

Improved preoperative risk stratification in endometrial carcinoma patients: external validation of the ENDORISK Bayesian network model in a large population-based case series.

Marcel Grube, Casper Reijnen, Peter J F Lucas, Frieder Kommoss, Felix K F Kommoss, Sara Y Brucker, Christina B Walter, Ernst Oberlechner, Bernhard Krämer, Jürgen Andress and 4 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07200466 (ENDORISK Clinical Implementation Study), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT07200466 narecruitingnot on this mapstarted 2025, after this paper: background citation

ENDORISK Clinical Implementation Study

TypeinterventionalSponsorRadboud University Medical CenterRan2025 to 2032Enrolled735ConditionsEndometrial CancerArmsENDORISK personalized risk assesment for lymph node metastases in endometrial cancer.
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Marcel GrubeDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Casper ReijnenDepartment of Radiation Oncology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
Peter J F LucasDepartment of Data Science, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands.
Frieder KommossInstitute of Pathology, Im Medizin Campus Bodensee, Röntgenstraße 2, 88048, Friedrichshafen, Germany.
Felix K F KommossInstitute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 672, 69120, Heidelberg, Germany.
Sara Y BruckerDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Christina B WalterDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Ernst OberlechnerDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Bernhard KrämerDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Jürgen AndressDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Felix NeisDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Annette StaeblerDepartment of Pathology and Neuropathology, University Hospital Tuebingen, Liebermeisterstraße 8, 72076, Tuebingen, Germany.
Johanna M A PijnenborgDepartment of Obstetrics and Gynaecology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
Stefan KommossDepartment of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany. Stefan.kommoss@med.uni-tuebingen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePreoperative risk stratification of newly diagnosed endometrial carcinoma (EC) patients has been hindered by only moderate prediction performance for many years. Recently ENDORISK, a Bayesian network model, showed high predictive performance. It was the aim of this study to validate ENDORISK by applying the model to a population-based case series of EC patients.

methodsENDORISK was applied to a retrospective cohort of women surgically treated for EC from 2003 to 2013. Prediction accuracy for LNM as well as 5-year DSS was investigated. The model's overall performance was quantified by the Brier score, discriminative performance by area under the curve (AUC).

resultsA complete dataset was evaluable from 247 patients. 78.1% cases were endometrioid histotype. The majority of patients (n = 156;63.2%) had stage IA disease. Overall, positive lymph nodes were found in 20 (8.1%) patients. Using ENDORISK predicted probabilities, most (n = 156;63.2%) patients have been assigned to low or very low risk group with a false-negative rate of 0.6%. AUC for LNM prediction was 0.851 [95% confidence interval (CI) 0.761-0.941] with a Brier score of 0.06. For 5-year DSS the AUC was 0.698 (95% CI 0.595-0.800) as Brier score has been calculated 0.09.

conclusionsWe were able to successfully validate ENDORISK for prediction of LNM and 5-year DSS. Next steps will now have to focus on ENDORISK performance in daily clinical practice. In addition, incorporating TCGA-derived molecular subtypes will be of key importance for future extended use. This study may support further promoting of data-based decision-making tools for personalized treatment of EC.

Indexed as

Endometrial NeoplasmsBayes TheoremFemaleHumansLymph NodesPrognosisRetrospective StudiesRisk AssessmentBayesian networkEndometrial carcinomaLymph nodesPersonalized medicineRisk stratification

Identifiers

PMID35939115
PMCPMC10314833

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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.