AI-Enhanced Diagnosis of Challenging Lesions in BreastMRI: A Methodology and Application Primer

Anke Meyer-Base, Lia Morra, Amirhessam Tahmassebi, Marc Lobbes, Uwe Meyer-Base, Katja Pinker*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

9 Citations (Web of Science)

Abstract

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a "second opinion" review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine-learning (ML) techniques. In this review article, we describe applications of ML-based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state-of-the-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. Level of Evidence 2 Technical Efficacy Stage 2

Original languageEnglish
Pages (from-to)686-702
Number of pages17
JournalJournal of Magnetic Resonance Imaging
Volume54
Issue number3
Early online date30 Aug 2020
DOIs
Publication statusPublished - Sep 2021

Keywords

  • computer-aided diagnosis systems
  • machine learning
  • kinetic features
  • morphologic features
  • magnetic resonance imaging
  • breast cancer
  • INDEPENDENT COMPONENT ANALYSIS
  • FUZZY CLUSTERING ALGORITHMS
  • COMPUTER-AIDED-DIAGNOSIS
  • DCE-MRI
  • CONTRAST ENHANCEMENT
  • MULTIPARAMETRIC MRI
  • PATTERN-RECOGNITION
  • AUTOMATED-ANALYSIS
  • NEURAL-NETWORKS
  • CANCER

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