Keywords: Diffusion Modeling, Microstructure, td-dMRI, OGSE, PGSE
Motivation: Precise diagnosis of endometrial cancer (EC) is essential to avoid unnecessary treatments and optimize patient outcomes. Current methods lack specificity in distinguishing benign from malignant lesions non-invasively.
Goal(s): This study assesses time-dependent diffusion MRI's ability to differentiate EC from normal endometrial tissue, focusing on microstructural imaging biomarkers.
Approach: A cohort of 22 patients underwent MRI using OGSE and PGSE sequences, and the diagnostic value of parameters like Vin and cellularity was evaluated.
Results: Vin showed the highest diagnostic performance (AUC 0.867), followed by cellularity (AUC 0.829), demostrating their potential as reliable indicators of EC.
Impact: Time-dependent diffusion MRI can non-invasively reveal microstructural characteristics of endometrial cancer, potentially improving diagnostic accuracy and informing preoperative planning, which may reduce invasive procedures and enhance treatment decision-making.
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