Abstract
Purpose: Breast cancer diagnosis relies on early detection and accurate subtype identification. This meta-analysis evaluated Diffusion-weighted imaging (DWI) and Intravoxel incoherent motion (IVIM)-DWI techniques for differentiating breast lesions, focusing on sensitivity, specificity, and limitations, to address inconsistencies and enhance diagnostic reliability.
Methods: Data from PubMed, Scopus, Embase, and Google Scholar (January 2013–September 2024) were systematically searched to assess the diagnostic accuracy of IVIM for breast lesions. Studies meeting the inclusion criteria underwent QUADAS-2 evaluation. Statistical analyses included SROC curves, heterogeneity assessment, leave-one-out meta-analysis, and publication bias evaluation via R packages and MetaBayesDTA.
Results: A total of 442 articles were identified; 26 were included in the review, and 23 were included in the meta-analysis. The diffusion coefficient (D), perfusion fraction (f), and apparent diffusion coefficient (ADC) values were significantly different between benign and malignant lesions. Among the parameters, the ADC and D exhibited the highest pooled sensitivity at 0.84 (95% CI: [0.77, 0.89]) across 15 studies, and 0.84 (95% CI: [0.79, 0.88]) across 19 studies, respectively. Also, D parameter demonstrated the highest pooled specificity at 0.80 (95% CI: [0.73, 0.85]).
Conclusion: Our meta-analysis identified D and ADC as the most current effective parameters for distinguishing benign from malignant breast lesions, warranting further research for comprehensive validation. Also, we outlined key methodological considerations in IVIM-DWI breast imaging, including sample size determination, b -value selection, ROI delineation, and menstrual status. However, careful planning, including b -value optimization and precise ROI placement, improves reliability in heterogeneous lesions, but the effect of hormonal fluctuations remains unclear.
| Original language | English |
|---|---|
| Article number | 110657 |
| Number of pages | 13 |
| Journal | Clinical Imaging |
| Volume | 129 |
| DOIs | |
| Publication status | Published - Jan 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Breast cancer
- Differential diagnosis
- IVIM-DWI
- Magnetic resonance imaging
- Meta-analysis
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