Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review

Abstract

Breast cancer remains a leading cause of morbidity and mortality among women worldwide. Early detection and precise diagnosis are critical for effective treatment and improved patient outcomes. This review explores the evolving role of radiology in the diagnosis and treatment of breast cancer, highlighting advancements in imaging technologies and the integration of artificial intelligence (AI). Traditional imaging modalities such as mammography, ultrasound, and magnetic resonance imaging have been the cornerstone of breast cancer diagnostics, with each modality offering unique advantages. The advent of radiomics, which involves extracting quantitative data from medical images, has further augmented the diagnostic capabilities of these modalities. AI, particularly deep learning algorithms, has shown potential in improving diagnostic accuracy and reducing observer variability across imaging modalities. AI-driven tools are increasingly being integrated into clinical workflows to assist in image interpretation, lesion classification, and treatment planning. Additionally, radiology plays a crucial role in guiding treatment decisions, particularly in the context of image-guided radiotherapy and monitoring response to neoadjuvant chemotherapy. The review also discusses the emerging field of theranostics, where diagnostic imaging is combined with therapeutic interventions to provide personalized cancer care. Despite these advancements, challenges such as the need for large annotated datasets and the integration of AI into clinical practice remain. The review concludes that while the role of radiology in breast cancer management is rapidly evolving, further research is required to fully realize the potential of these technologies in improving patient outcomes.

Keywords: artificial intelligence; breast cancer; deep learning; image-guided therapy; mammography; mri; radiology; radiomics; theranostics; ultrasound imaging.

Figure 1. The image displays the radiomics workflow.

I Step: ROIs are segmented automatically as the first step. In this instance, an ROI has been identified and outlined around a worrisome lesion. II Step: radiomic characteristics are derived from this ROI during the second stage. The features include the form of the tumor, as well as statistical characteristics generated from the image intensity histogram (referred to as first-order statistics features) and additional statistical characteristics known as “texture features” (referred to as second-order statistics features). III Step: in the third stage, radiomics characteristics are adjusted to eliminate redundancy. Significant quantitative characteristics are further examined using statistical techniques or machine learning algorithms to get a clinically understandable result. Reproduced with permission from Conti et al. [17] ROIs, region of interest

Figure 2. Hybrid CNN-LSTM model for bimodal BC classification.

Reproduced with permission from Atrey et al. [29] BC, breast cancer; CNN, Convolutional Neural Network; LSTM, long short-term memory; ROI, region of interest

Figure 3. Risk of breast cancer related death in women carrying a BRCA1 or BRCA2 variant, broken down by MRI surveillance status over a 20-year period.

Reproduced under the terms of the CC-BY License from Lubinski et al. [48]