Abstract
Objectives: To assess the diagnostic performance of ultrafast magnetic resonance imaging (UF-DCE MRI) in differentiating benign from malignant breast lesions. Materials and methods: A comprehensive search was conducted until September 1, 2023, in Medline, Embase, and Cochrane databases. Clinical studies evaluating the diagnostic performance of UF-DCE MRI in breast lesion stratification were screened and included in the meta-analysis. Pooled summary estimates for sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchic summary operating characteristics (SROC) curves were pooled under the random-effects model. Publication bias and heterogeneity between studies were calculated. Results: A final set of 16 studies analyzing 2090 lesions met the inclusion criteria and were incorporated into the meta-analysis. Using UF-DCE MRI kinetic parameters, the pooled sensitivity, specificity, DOR, and area under the curve (AUC) for differentiating benign from malignant breast lesions were 83% (95% CI 79–88%), 77% (95% CI 72–83%), 18.9 (95% CI 13.7–26.2), and 0.876 (95% CI 0.83–0.887), respectively. We found no significant difference in diagnostic accuracy between the two main UF-DCE MRI kinetic parameters, maximum slope (MS) and time to enhancement (TTE). DOR and SROC exhibited low heterogeneity across the included studies. No evidence of publication bias was identified (p = 0.585). Conclusions: UF-DCE MRI as a stand-alone technique has high accuracy in discriminating benign from malignant breast lesions. Clinical relevance statement: UF-DCE MRI has the potential to obtain kinetic information and stratify breast lesions accurately while decreasing scan times, which may offer significant benefit to patients. Key Points: • Ultrafast breast MRI is a novel technique which captures kinetic information with very high temporal resolution. • The kinetic parameters of ultrafast breast MRI demonstrate a high level of accuracy in distinguishing between benign and malignant breast lesions. • There is no significant difference in accuracy between maximum slope and time to enhancement kinetic parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 6285-6295 |
| Number of pages | 11 |
| Journal | European Radiology |
| Volume | 34 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2024 |
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
- Artificial intelligence
- Breast cancer
- Kinetics
- Magnetic resonance imaging
- Ultrafast
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