  Current imaging methods for diagnosing BC are associated with limited
sensitivity and specificity and modest positive predictive power. The recent
progress in image analysis using artificial intelligence (AI) has created great
promise to improve breast cancer (BC) diagnosis and subtype differentiation. In
this case, novel quantitative computational methods, such as radiomics, have
been developed to improve the sensitivity and specificity of early BC diagnosis
and classification. The potential of radiomics in improving the diagnostic
efficacy of imaging studies has been shown in several studies. In this review
article, we discuss the radiomics workflow and current hand-crafted radiomics
methods in the diagnosis and classification of BC based on most recent studies
on different imaging modalities, e.g. MRI, mammography, contrast-enhanced
spectral mammography (CESM), ultrasound imaging, and digital breast
tumosynthesis (DBT). We also discuss current challenges and potential
strategies to improve the specificity and sensitivity of radiomics in breast
cancer to help achieve a higher level of BC classification and diagnosis in the
clinical setting. The growing field of AI incorporation with imaging
information has opened a great opportunity to provide a higher level of care
for BC patients.
