MRI and Endometrial Cancer After FIGO 2023—What’s New? A Narrative Review

TL;DR

This paper reviews how MRI is being adapted to support new biological-based staging of endometrial cancer under the 2023 FIGO system.

Contribution

The paper highlights how modern MRI techniques can now provide biological insights, aligning imaging with molecular classifications in endometrial cancer.

Findings

Multiparametric MRI remains the standard for local staging of endometrial cancer.

Quantitative diffusion MRI provides microstructural biomarkers linked to tumor aggressiveness and prognosis.

Radiomics and AI models accurately predict lymphovascular invasion, nodal metastasis, and molecular subtypes.

Abstract

Endometrial cancer is the most common gynecologic cancer in developed countries, and its management is changing due to the introduction of the 2023 FIGO staging system, which places greater emphasis on tumor biology rather than anatomy alone. This review explains why magnetic resonance imaging is being reconsidered in this new context. The authors aim to describe how recent advances in MRI techniques can support more accurate diagnosis, risk assessment, and treatment planning. Beyond showing how far a tumor has spread, modern MRI methods can provide information related to tumor aggressiveness and prognosis through advanced diffusion imaging, standardized lymph node evaluation, and computer-based image analysis. These developments may help bridge the gap between imaging and molecular classification, offering non-invasive tools to guide personalized care and future research in endometrial cancer.

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.

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