Abstract
Background: Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) is a noncontrast technique estimating diffusion and perfusion parameters via multiple b-values, essential for oncology imaging. However, there is limited collective evidence regarding the efficacy of IVIM in oncology imaging compared to contrast-enhanced (CE) MRI perfusion techniques. This systematic review and meta-analysis compared IVIM′s diagnostic accuracy and correlation with CE MRI perfusion techniques. Methods: Following PRISMA guidelines (PROSPERO-registered), a literature search across five databases (PubMed, Scopus, Embase, Web of Science, and Cochrane Library) was conducted. Diagnostic metrics, including AUC, sensitivity, specificity, and correlation coefficients, were analyzed using a random-effects model, with heterogeneity and publication bias assessed via I2 statistics and Egger′s test. Results: Eighteen studies on breast, rectal, and brain cancers were analyzed. For breast cancer, IVIM showed 83.50% sensitivity and 81.24% specificity compared to dynamic contrast-enhanced (DCE) MRI′s 88.04% sensitivity and 65.98% specificity. In rectal cancer, IVIM achieved 70.9% sensitivity and 56.2% specificity, outperforming DCE MRI′s 58.11% sensitivity and 72.49% specificity. For gliomas, IVIM demonstrated 92.27% sensitivity and 74.06% specificity compared to dynamic susceptibility contrast (DSC) MRI′s 95.71% sensitivity and 92.91% specificity. Correlations between IVIM and CE parameters were weak to moderate. Conclusion: IVIM demonstrated equal or superior diagnostic performance to CE MRI in breast cancer, rectal cancer, and gliomas, offering a noncontrast alternative. However, unclear parameter correlations warrant future studies focusing on IVIM protocol optimization based on perfusion regimes.
| Original language | English |
|---|---|
| Article number | 3621023 |
| Journal | International Journal of Biomedical Imaging |
| Volume | 2025 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 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
- Radiology Nuclear Medicine and imaging
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