MARATTO

article · EAS Journal of Radiology and Imaging Technology

Retrospective Study on the Place of 1.5 Tesla MRI in the Management of Breast Pathology in Douala

2025Open accessUniversity of Buea

Abstract

Aim: Magnetic resonance imaging is the most sensitive imaging technique for the detection of breast cancer, although it is rarely requested as a first-line procedure. The aim of our study was to determine the indications and results of breast examinations using 1.5 Tesla MRI in a referral imaging centre in Douala. Methodology: We conducted a retrospective descriptive cross-sectional study between 01 March 2021 and 31 December 2022. The examinations were performed with a dedicated breast scanner, using axial and sagittal T1 SE and T2 FSE sequences, as well as dynamic T1 sequences with gadolinium injection, and interpreted by two experienced radiologists. According to the BIRADS classification of the American College of Radiology (ACR), examinations graded 4 to 5 were considered positive. Results: Thirty-five patients underwent breast MRI, with a median age of 47, ranging from 29 to 65 years. The vast majority of prescribers were gynaecologists (88.6%). The most common indication for MRI was for additional assessment of a lesion (34.2%), followed by assessment of locoregional extension (17.1%), particularly for multiple extensions. All investigations requested solely for mastodynia (14.3%) were unremarkable. The two cases of investigation of breast discharge were mainly associated with enhancement without mass (5.7%). At the end of the study, 28.5% of MRI scans were pathological according to BIRADS. Conclusion: The gradual introduction of 1.5 T MRI scanners in our environment means that we can now carry out breast MRI scans that are much more sensitive than ultrasound mammography. However, prescriptions are still highly specialised, and indications need to be verified, given their relative specificity and the socio-economic context in which our countries are evolving.

Research topics

  • MRI in cancer diagnosis
  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.36349/easjrit.2025.v07i01.006

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.