Publications

Authors:

Altabella, Luisa; Benetti, Giulio; Camera, Lucia; Cardano, Giuseppe; Montemezzi, Stefania; Cavedon, Carlo

Title:

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

Year:

2022

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Format:

Elettronico

Referee:

No

Name of journal:

PHYSICS IN MEDICINE AND BIOLOGY

ISSN of journal:

0031-9155

N° Volume:

67

Number or Folder:

Jul 20

Page numbers:

1-15

Keyword:

breast cancer; breast lesion classification; breast magnetic resonance imaging; machine learning; radiomics

Short description of contents:

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.

Product ID:

128064

Handle IRIS:

11562/1071549

Last Modified:

February 23, 2023

Bibliographic citation:

Altabella, Luisa; Benetti, Giulio; Camera, Lucia; Cardano, Giuseppe; Montemezzi, Stefania; Cavedon, Carlo, Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification «PHYSICS IN MEDICINE AND BIOLOGY» , vol. 67 , n. Jul 20 , 2022 , pp. 1-15

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