Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

Nazimah Ab Mumin; Marlina Tanty Ramli Hamid; Jeannie Hsiu Ding Wong; Kartini Rahmat; Kwan Hoong Ng

doi:10.1016/j.acra.2021.07.017

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

Acad Radiol. 2022 Jan:29 Suppl 1:S89-S106. doi: 10.1016/j.acra.2021.07.017. Epub 2021 Sep 2.

Authors

Nazimah Ab Mumin¹, Marlina Tanty Ramli Hamid², Jeannie Hsiu Ding Wong³, Kartini Rahmat⁴, Kwan Hoong Ng³

Affiliations

¹ Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
² Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
³ Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
⁴ Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. Electronic address: katt_xr2000@yahoo.com.

PMID: 34481705
DOI: 10.1016/j.acra.2021.07.017

Abstract

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.

Keywords: Dynamic contrast-enhanced; Invasive breast cancer; Magnetic resonance imaging; Molecular subtypes.

Publication types

Research Support, Non-U.S. Gov't
Systematic Review

MeSH terms

Artificial Intelligence*
Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / pathology
Diffusion Magnetic Resonance Imaging* / methods
Female
Humans
Phenotype
Retrospective Studies