Evidence map›Paper›PMID 35000020›Full record

ArticleInsights into imaging2022

MRI-assessed tumor-free distance to serosa predicts deep myometrial invasion and poor outcome in endometrial cancer.

Julie Andrea Dybvik, Kristine E Fasmer, Sigmund Ytre-Hauge, Jenny Hild Aase Husby, Øyvind O Salvesen, Ingunn Marie Stefansson, Camilla Krakstad, Jone Trovik, Ingfrid S Haldorsen

Open access · goldAbstract read
In one paragraph

Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.8field-weighted citation impact, top 10% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Article
  3. Staging by imaging in gynecologic cancer and the role of ultrasound: an update of European joint consensus statements.International journal of gynecological cancer : official journal of the International Gynecological Cancer Society · 2024
    Review
  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

9 authors at 2 institutions in 1 country.

Julie Andrea DybvikMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway. julie.andrea.dybvik@helse-bergen.no.ORCID http://orcid.org/0000-0003-1377-4999
Kristine E FasmerMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Sigmund Ytre-HaugeMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Jenny Hild Aase HusbyMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Øyvind O SalvesenUnit for Applied Clinical Research, Department of Public Health and Nursing, Norwegian University of Science and Technology, Post Office Box 8905, 7491, Trondheim, Norway.
Ingunn Marie StefanssonDepartment of Pathology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Camilla KrakstadDepartment of Obstetrics and Gynaecology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Jone TrovikDepartment of Obstetrics and Gynaecology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Ingfrid S HaldorsenMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Post Office Box 1400, 5021, Bergen, Norway.
Haukeland University Hospital · NONorwegian University of Science and Technology · NO

Funding

helse vest 912285trond mohn foundation (no) BFS2018TMT06
6 · The paper itself

Abstract

objectivesTo explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC).

methodsPreoperative pelvic MRI of 357 included patients with histologically confirmed EC were read independently by three radiologists blinded to clinical information. The radiologists recorded imaging findings (T1 post-contrast sequence) suggesting deep (≥ 50%) myometrial invasion (iDMI) and measured anteroposterior tumor diameter (APD), depth of myometrial tumor invasion (DOI) and tumor-free distance to serosa (iTFD). Receiver operating characteristic (ROC) curves for the prediction of pDMI were plotted for the different MRI measurements. The predictive and prognostic value of the MRI measurements was analyzed using logistic regression and Cox proportional hazard model.

resultsiTFD yielded highest area under the ROC curve (AUC) for the prediction of pDMI with an AUC of 0.82, whereas DOI, APD and iDMI yielded AUCs of 0.74, 0.81 and 0.74, respectively. Multivariate analysis for predicting pDMI yielded highest predictive value of iTFD <  6 mm with OR of 5.8 (p < 0.001) and lower figures for DOI ≥ 5 mm (OR = 2.8, p = 0.01), APD ≥ 17 mm (OR = 2.8, p < 0.001) and iDMI (OR = 1.1, p = 0.82). Patients with iTFD < 6 mm also had significantly reduced progression-free survival with hazard ratio of 2.4 (p < 0.001).

conclusionFor predicting pDMI, iTFD yielded best diagnostic performance and iTFD < 6 mm outperformed other cutoff-based imaging markers and conventional subjective assessment of deep myometrial invasion (iDMI) for diagnosing pDMI. Thus, iTFD at MRI represents a promising preoperative imaging biomarker that may aid in predicting pDMI and high-risk disease in EC.

Indexed as

BiomarkersEndometrial neoplasmMagnetic resonance imagingProgression-free survivalRadiologists

Identifiers

PMID35000020
PMCPMC8742796
OpenAlexW4205739105

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.