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Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

Authors
  • Altabella, Luisa
  • Benetti, Giulio
  • Camera, Lucia
  • Cardano, Giuseppe
  • Montemezzi, Stefania
  • Cavedon, Carlo
Type
Published Article
Journal
Physics in Medicine and Biology
Publisher
IOP Publishing
Publication Date
Jul 20, 2022
Volume
67
Issue
15
Identifiers
DOI: 10.1088/1361-6560/ac7d8f
Source
ioppublishing
Keywords
Disciplines
  • Topical Review
License
Unknown

Abstract

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment. Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

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