Objective
Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting
breast cancer. The purpose of this systematic review is to investigate the role of
human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.
Methods
We performed a literature search of published articles on the application of MRI phenotypic
features in invasive breast cancer molecular subtype classifications by radiologists'
interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000
to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion
criteria.
Results
All studies were case-controlled, retrospective study and research-based. The majority
of the studies assessed the MRI features using American College of Radiology- Breast
Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced
(DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence.
Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal
B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary
of the radiologists' extracted breast MRI phenotypical features and their correlating
breast cancer subtypes classifications. The characteristic features are morphology,
enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the
most distinctive MRI features compared to the other subtypes and luminal A and B have
many similar features.
Conclusion
The MRI features which are predictive of each subtype are the morphology, internal
enhancement features, and T2 signal intensity, predominantly between TN and the rest.
Radiologists’ visual interpretation of some of MRI features may offer insight into
the respective invasive breast cancer molecular subtype. However, current evidence
are still limited to “suggestive” features instead of a diagnostic standard. Further
research is recommended to explore this potential application, for example, by augmentation
of radiologists’ visual interpretation by artificial intelligence.
Key words
Abbreviations:
ACR (American College of Radiology), ADC (apparent diffusion coefficient), AUC (area under curve), BI-RADS (Breast Imaging Reporting and Data System), BPE (breast parenchymal enhancement), CEP17 (chromosome 17 centromere), DWI (diffusion weighted imaging), DCE (dynamic contrast enhancement), Dt (diffusion coefficient), ER (oestrogen receptor), HER2 (human epidermal growth factor receptor -2), IHC (immunohistochemical), Lum A (Luminal A), Lum B (Luminal B), MRI (magnetic resonance imaging), NME (non mass enhancement), OR (odds ratio), PR (progesterone receptor), PRISMA (preferred reporting items for systemic review and meta-analyses), ROI (region of interest), SI (signal intensity), STIR (Short-T1 inversion recovery), TCGA (the cancer genome atlas), TN (triple negative), TTP (time to peak), tCho (total Choline), VEGF (vascular endothelial growth factor)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: September 02, 2021
Accepted:
July 20,
2021
Received in revised form:
July 14,
2021
Received:
June 2,
2021
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
