Evidence map›Paper›PMID 41899606›Full record

ReviewCancers2026

MRI and Endometrial Cancer After FIGO 2023-What's New? A Narrative Review.

Marco Gennarini, Roberta Valerieva Ninkova, Valentina Miceli, Federica Curti, Sandrine Riccardi, Benedetta Gui, Stefania Rizzo, Aradhana M Venkatesan, Stephanie Nougaret, Lucia Manganaro

Abstract readReview
In one paragraph

Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Marco GennariniDepartment of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.
Roberta Valerieva NinkovaDepartment of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0009-0003-7191-693X
Valentina MiceliDepartment of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy.
Federica CurtiDepartment of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy.
Sandrine RiccardiDepartment of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy.
Benedetta GuiDipartimento Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.ORCID 0000-0002-5130-2100
Stefania RizzoImaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale (EOC), 6900 Lugano, Switzerland.ORCID 0000-0002-5151-0866
Aradhana M VenkatesanDivision of Diagnostic Imaging, Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0002-5033-0820
Stephanie NougaretPINKCC Lab, INSERM, Montpellier Cancer Research Institute, University of Montpellier, 34298 Montpellier, France.
Lucia ManganaroDepartment of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0001-9037-9904

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometrial cancer (EC) is the most common gynaecologic malignancy in developed countries, and its diagnostic and prognostic framework has evolved substantially following the introduction of the 2023 FIGO staging system, which integrates molecular classification with clinicopathologic features. Both histopathologic features, such as lymphovascular space invasion (LVSI) and molecular subtype, including POLE mutation status, mismatch-repair deficiency, and p53-abnormal phenotype, are incorporated into the updated staging system, highlighting the importance of tumour biology in risk stratification. Accordingly, the value and contribution of MRI to patient management must extend beyond macroscopic assessment to support a more biologically driven approach. This narrative review synthesizes recent advances in MRI for EC, highlighting developments that improve diagnostic accuracy and align imaging with the molecular paradigm. Multiparametric MRI remains the reference standard for local staging, while emerging quantitative diffusion techniques provide microstructural biomarkers associated with tumor aggressiveness and prognostic features. The consistency of nodal staging has been enhanced by Node-RADS, a structured reporting system that integrates nodal morphology and configuration, with the goal of improving reproducibility and diagnostic performance over size-based assessment alone. Radiomics and artificial intelligence (AI) represent the most transformative frontier, enabling MRI to infer biological behaviours previously accessible only via histopathologic assessment. Radiomics and deep-learning models have demonstrated high accuracy in predicting LVSI, DMI, nodal metastasis, and molecular subtypes, offering non-invasive biomarkers aligned with FIGO 2023 prognostic categories. Together, these advances position MRI as a quantitatively enriched, biologically relevant tool that supports precision oncology in endometrial cancer.

Indexed as

artificial intelligencediffusion weighted imagingendometrial cancermagnetic resonance imagingradiomic

Identifiers

PMID41899606
PMCPMC13025262

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.