An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis

TL;DR

This paper reviews recent advances in radiomics-based AI methods for breast cancer diagnosis, highlighting workflows, current techniques, challenges, and future strategies to enhance diagnostic accuracy across various imaging modalities.

Contribution

It provides an updated overview of radiomics techniques and discusses strategies to improve AI-driven breast cancer diagnosis and classification.

Findings

Radiomics improves sensitivity and specificity in BC diagnosis.

Current methods are applied across multiple imaging modalities.

Challenges include enhancing specificity and clinical integration.

Abstract

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.