Evidence mapPaperPMID 40576671Full record

ArticleAbdominal radiology (New York)2026

Preoperative risk assessment of endometrial cancer using histogram analysis of weighted and quantitative MRI images.

Magnus Palmér, Åsa Åkesson, Maria Ljungberg, Stefan Kuczera, Emilia Gryska, Erica de Coursey, Rolf A Heckemann, Pernilla Dahm Kähler, Stephan E Maier, Henrik Leonhardt

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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

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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

3 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Magnus PalmérUniversity of Gothenburg, Gothenburg, Sweden. carl.magnus.palmer@gu.se.
Åsa ÅkessonUniversity of Gothenburg, Gothenburg, Sweden.
Maria LjungbergUniversity of Gothenburg, Gothenburg, Sweden.
Stefan KuczeraUniversity of Gothenburg, Gothenburg, Sweden.
Emilia GryskaUniversity of Gothenburg, Gothenburg, Sweden.
Erica de CourseySahlgrenska University Hospital, Gothenburg, Sweden.
Rolf A HeckemannUniversity of Gothenburg, Gothenburg, Sweden.
Pernilla Dahm KählerUniversity of Gothenburg, Gothenburg, Sweden.
Stephan E MaierUniversity of Gothenburg, Gothenburg, Sweden.
Henrik LeonhardtUniversity of Gothenburg, Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aim of this study was to evaluate the capability of histogram analysis of weighted and quantitative MRI images to improve preoperative endometrial cancer (EC) risk stratification by providing information about the histological properties of the tumours.

methodsIn this prospective study, 94 patients with biopsy verified endometrial carcinoma underwent a preoperative MRI examination performed according to the European Society of Urogenital Radiology (ESUR) guidelines with addition of synthetic MRI, dynamic contrast enhancement and diffusion weighted imaging (DWI) with high b-values. Quantitative relaxation maps, perfusion maps and diffusion kurtosis imaging (DKI) maps were generated from the additional sequences. Tumours were segmented on three adjacent slices and histogram properties were compared between tumours with low and high histological risk.

resultsSignificant differences were found between tumours with low and high histological risk in the histogram properties for the DKI derived apparent diffusion maps (D

conclusionHistogram analysis of DKI shows better potential to discriminate between EC histological risk groups and histologically determined endometrioid tumour grades than regular DWI, relaxation maps from synthetic MR, perfusion maps and T1 or T2 weighted images.

Indexed as

Endometrial NeoplasmsMagnetic Resonance ImagingPreoperative CareAdultAgedAged, 80 and overContrast MediaDiffusion Magnetic Resonance ImagingFemaleHumansImage Interpretation, Computer-AssistedMiddle AgedProspective StudiesRisk AssessmentContrast MediaDiffusion MRIEndometrial cancerMagnetic resonance imagingPerfusion MRI

Identifiers

PMID40576671
PMCPMC12830399

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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 Socratic graph.