The diagnostic performance of ultrafast MRI to differentiate benign from malignant breast lesions: a systematic review and meta-analysis

Yoav Amitai; Vivianne A R Freitas; Orit Golan; Rivka Kessner; Tamar Shalmon; Rina Neeman; Michal Mauda-Havakuk; Diego Mercer; Miri Sklair-Levy; Tehillah S Menes

doi:10.1007/s00330-024-10690-y

The diagnostic performance of ultrafast MRI to differentiate benign from malignant breast lesions: a systematic review and meta-analysis

Eur Radiol. 2024 Mar 21. doi: 10.1007/s00330-024-10690-y. Online ahead of print.

Authors

Affiliations

¹ Department of Medical Imaging, Tel Aviv University, Sackler School of Medicine, Sourasky Medical Center, Weizmann 6, 6423906, Tel Aviv-Yafo, Israel. yoavslava@yahoo.com.
² Joint Department of Medical Imaging - University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue - M5G 2M9, Toronto, Ontario, Canada.
³ Department of Medical Imaging, Tel Aviv University, Sackler School of Medicine, Sourasky Medical Center, Weizmann 6, 6423906, Tel Aviv-Yafo, Israel.
⁴ Department of Medical Imaging, Sackler School of Medicine, Chaim Sheba Medical Center, Tel Aviv University, Tel Hashomer, Derech Shiba 2, 52621, Ramat-Gan, Israel.
⁵ Department of Surgery, Sackler School of Medicine, Chaim Sheba Medical Center, Tel Aviv University, Tel Hashomer, Derech Shiba 2, 52621, Ramat-Gan, Israel.

PMID: 38512492
DOI: 10.1007/s00330-024-10690-y

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.

Keywords: Artificial intelligence; Breast cancer; Kinetics; Magnetic resonance imaging; Ultrafast.

Publication types

Review