AI‐Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer

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

AI-enhanced breast imaging: Where are we and where are we heading?

“…This is particularly important in the setting of challenging lesions such as sub-centimeter lesions, non-mass like lesions, or those pertaining to high-risk patient groups. AI has shown potential to improve the diagnosis of these type of lesions in breast MRI [57]. For example, Lo Gullo et al [58] evaluated sub-centimeter enhancing lesions in BRCA mutation carriers and showed that radiomics/ML improves diagnostic accuracy and could be used as an adjunct to spare unnecessary biopsies for benign-appearing small breast masses in this population (Figs.…”

AI-enhanced Synchronized Multiparametric 18F-FDG PET/MRI for Accurate Breast Cancer Diagnosis

“…A maximum of 5 features were selected to avoid over tting. Diagnostic models were then developed in MATLAB using a ne gaussian support vector machine (SVM), one of the most employed ML classi ers in medical imaging [23] and a 5-fold cross-validation. Data were initially standardized to prevent any particular dependence on an individual parameter.…”

Artificial Intelligence and Big Data

“…Both modalities play a role primarily in oncological imaging. CADe techniques are used in the detection of pulmonary masses [5], prostate malignancies [35], breast cancer [3] and malignant brain tumours [36]. However, they have not yet found their way into ophthalmologic radiology.…”

“…The term "machine learning" is a generic term for computer programmes that "learn" from sample data and can automatically improve through these "successes". On the one hand, this allows the identification of complex pathologic patterns, such as certain tumours [3]. However, it is also possible to quantify these changes.…”