Abstract
Introduction: Proper clinical management requires accurate differentiation of benign and malignant endometrial lesions. Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI) are non-invasive MRI methods that have emerged as key tools in this differentiation. This systematic review compares the diagnostic accuracy of IVIM and DKI in evaluating endometrial lesions. Methods: A systematic review of the literature was conducted using pertinent databases. Inclusion criteria were studies that compared IVIM and DKI for the differentiation of benign and malignant endometrial lesions and provided diagnostic performance statistics like area under the curve (AUC), specificity, and sensitivity. Meta-analysis was not performed in this review due to heterogeneity of the included studies, such as variation in b-values, magnetic field strengths of the MRI scanners, no standardized scanning parameters, and differences in Region of interest (ROI) selection as reported in the articles. The risk of bias was assessed using the QUADAS-2 tool. Results: In total, 11 studies were included, which evaluated endometrial lesions. F, D, D∗, MD, and MK IVIM and DKI parameters had different diagnostic performances, with AUC ranging from 0.601 to 0.98. Sensitivity and specificity also differed widely between studies. The majority of studies used Echo Planar Imaging (EPI) sequences. Risk of bias assessment indicated moderate-to-high methodological quality. Conclusion: IVIM and DKI could offer promising non-invasive methods for distinguishing endometrial lesions. Yet, differences in methodology and heterogeneity of studies emphasize the necessity for further standardization. Implication of practice: IVIM and DKI MRI can be used as non-invasive quantitative tools to provide insights about tissue microstructure and perfusion characteristics which help clinicians in characterization of endometrial cancers.
| Original language | English |
|---|---|
| Article number | 103125 |
| Journal | Radiography |
| Volume | 31 |
| DOIs | |
| Publication status | Published - 12-2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Research and Theory
- Radiological and Ultrasound Technology
- Health Professions (miscellaneous)
- Radiology Nuclear Medicine and imaging
- Assessment and Diagnosis
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