Multiparametric MRI for preoperative identification of aggressive type endometrial carcinoma in FIGO 2023: Integrating intratumoral and peritumoral parameters from multi-b-value diffusion-weighted imaging and dynamic contrast-enhanced MRI
- PMID: 41729871
- DOI: 10.1093/bjr/tqag032
Multiparametric MRI for preoperative identification of aggressive type endometrial carcinoma in FIGO 2023: Integrating intratumoral and peritumoral parameters from multi-b-value diffusion-weighted imaging and dynamic contrast-enhanced MRI
Abstract
Objectives: To evaluate a multiparametric MRI protocol encompassing intravoxel incoherent motion diffusion-weighted imaging, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI for discriminating aggressive (AEC) from non-aggressive type endometrial carcinoma (NAEC) according to the FIGO 2023 staging system.
Methods: This study involved retrospective analyses of a prospective dataset. 112 consecutive patients (77 NAEC and 35 AEC) underwent multiparametric MRI. Intratumoral and peritumoral quantitative MRI parameters were calculated. A multivariate logistic regression model comprising clinical data, conventional MRI features, and quantitative MRI metrics was constructed. Model performance was evaluated using receiver operating characteristic analysis, calibration curves, and bootstrap resampling (n = 1000).
Results: AEC demonstrated significantly lower perfusion fraction (f) and mean diffusivity (MD), but higher pseudo diffusion coefficient (D*) and peritumoral D* (D*_peri) compared to NAEC (all p < 0.05). Multivariate analysis identified f, peritumoral mean kurtosis (MK_peri), and peritumoral maximum slope of increase (MaxSlope_peri) as independent predictors of AEC (AUC = 0.791, 95% CI: 0.692-0.891). Integration of menopause status, tumor location and extension beyond corpus, and quantitative MRI parameters yielded a combined model with a stratified bootstrap AUC of 0.830 (95% CI: 0.800-0.846), particularly for FIGO 2023 stage I-II patients (AUC = 0.832, 95%CI: 0.742-0.922). Significant differences in D*, D*_peri, f, and MD were observed among NAEC with and without squamous differentiation and AEC groups.
Conclusions: Multiparametric MRI, incorporating advanced quantitative sequences and conventional MRI features, could help effectively predict AEC before surgery.
Advances in knowledge: The study bridges advanced imaging with the updated FIGO 2023 staging system, potentially offering a non-invasive assessment tool for endometrial carcinoma management.
Keywords: Diffusion magnetic resonance imaging; Endometrial neoplasms; Multiparametric magnetic resonance imaging; Perfusion.
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